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Journal articles on the topic 'Microbial association networks'

Author: Grafiati

Published: 25 May 2024

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1

Lo, Chieh, and Radu Marculescu. "MPLasso: Inferring microbial association networks using prior microbial knowledge." PLOS Computational Biology 13, no. 12 (2017): e1005915. http://dx.doi.org/10.1371/journal.pcbi.1005915.

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Rocha-Viggiano, Ana K., Saray Aranda-Romo, Mariana Salgado-Bustamante, and Cesaré Ovando-Vázquez. "Meconium Microbiota Composition and Association with Birth Delivery Mode." Advanced Gut & Microbiome Research 2022 (November 7, 2022): 1–18. http://dx.doi.org/10.1155/2022/6077912.

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Recently, the intrauterine sterile environment theory has been questioned. Growing evidence shows that microbial in utero pioneer gut colonization could occur prebirth, and this initial colonization may play an important role in the development of the neonate immune system and setting up a niche for the adult-like microbiota. In this study, we compared the microbiota networks from public available meconium datasets from different countries. The findings showed differences at the genera level and were country-dependent. We generated and analyzed bacterial networks, at the genera level of meconi

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Centler, Florian, Sarah Günnigmann, Ingo Fetzer, and Annelie Wendeberg. "Keystone Species and Modularity in Microbial Hydrocarbon Degradation Uncovered by Network Analysis and Association Rule Mining." Microorganisms 8, no. 2 (2020): 190. http://dx.doi.org/10.3390/microorganisms8020190.

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Natural microbial communities in soils are highly diverse, allowing for rich networks of microbial interactions to unfold. Identifying key players in these networks is difficult as the distribution of microbial diversity at the local scale is typically non-uniform, and is the outcome of both abiotic environmental factors and microbial interactions. Here, using spatially resolved microbial presence-absence data along an aquifer transect contaminated with hydrocarbons, we combined co-occurrence analysis with association rule mining to identify potential keystone species along the hydrocarbon deg

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Ai, Dongmei, Hongfei Pan, Xiaoxin Li, Min Wu, and Li C. Xia. "Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls." PeerJ 7 (July 29, 2019): e7315. http://dx.doi.org/10.7717/peerj.7315.

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The human gut microbiota plays a major role in maintaining human health and was recently recognized as a promising target for disease prevention and treatment. Many diseases are traceable to microbiota dysbiosis, implicating altered gut microbial ecosystems, or, in many cases, disrupted microbial enzymes carrying out essential physio-biochemical reactions. Thus, the changes of essential microbial enzyme levels may predict human disorders. With the rapid development of high-throughput sequencing technologies, metagenomics analysis has emerged as an important method to explore the microbial comm

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Faust, Karoline, and Jeroen Raes. "CoNet app: inference of biological association networks using Cytoscape." F1000Research 5 (June 27, 2016): 1519. http://dx.doi.org/10.12688/f1000research.9050.1.

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Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequenc

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Faust, Karoline, and Jeroen Raes. "CoNet app: inference of biological association networks using Cytoscape." F1000Research 5 (October 14, 2016): 1519. http://dx.doi.org/10.12688/f1000research.9050.2.

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Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequenc

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Nagpal, Sunil, Rashmi Singh, Deepak Yadav, and Sharmila S. Mande. "MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks." Nucleic Acids Research 48, W1 (2020): W572—W579. http://dx.doi.org/10.1093/nar/gkaa254.

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Abstract Microbial association networks are frequently used for understanding and comparing community dynamics from microbiome datasets. Inferring microbial correlations for such networks and obtaining meaningful biological insights, however, requires a lengthy data management workflow, choice of appropriate methods, statistical computations, followed by a different pipeline for suitably visualizing, reporting and comparing the associations. The complexity is further increased with the added dimension of multi-group ‘meta-data’ and ‘inter-omic’ functional profiles that are often associated wit

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Liu, Fei, Shao-Wu Zhang, Ze-Gang Wei, Wei Chen, and Chen Zhou. "Mining Seasonal Marine Microbial Pattern with Greedy Heuristic Clustering and Symmetrical Nonnegative Matrix Factorization." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/189590.

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With the development of high-throughput and low-cost sequencing technology, a large number of marine microbial sequences were generated. The association patterns between marine microbial species and environment factors are hidden in these large amount sequences. Mining these association patterns is beneficial to exploit the marine resources. However, very few marine microbial association patterns are well investigated in this field. The present study reports the development of a novel method called HC-sNMF to detect the marine microbial association patterns. The results show that the four seas

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Poudel, R., A. Jumpponen, D. C. Schlatter, et al. "Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management." Phytopathology® 106, no. 10 (2016): 1083–96. http://dx.doi.org/10.1094/phyto-02-16-0058-fi.

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Network models of soil and plant microbiomes provide new opportunities for enhancing disease management, but also challenges for interpretation. We present a framework for interpreting microbiome networks, illustrating how observed network structures can be used to generate testable hypotheses about candidate microbes affecting plant health. The framework includes four types of network analyses. “General network analysis” identifies candidate taxa for maintaining an existing microbial community. “Host-focused analysis” includes a node representing a plant response such as yield, identifying ta

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Avila-Jimenez, Maria-Luisa, Gavin Burns, Zhili He, et al. "Functional Associations and Resilience in Microbial Communities." Microorganisms 8, no. 6 (2020): 951. http://dx.doi.org/10.3390/microorganisms8060951.

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Microbial communities have inherently high levels of metabolic flexibility and functional redundancy, yet the structure of microbial communities can change rapidly with environmental perturbation. To understand whether such changes observed at the taxonomic level translate into differences at the functional level, we analyzed the structure of taxonomic and functional gene distribution across Arctic and Antarctic locations. Taxonomic diversity (in terms of alpha diversity and species richness) differed significantly with location. However, we found that functional genes distributed evenly acros

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Yu, Jingjing, Wei Cong, Yi Ding, Lixiao Jin, Jing Cong, and Yuguang Zhang. "Interkingdom Plant–Soil Microbial Ecological Network Analysis under Different Anthropogenic Impacts in a Tropical Rainforest." Forests 13, no. 8 (2022): 1167. http://dx.doi.org/10.3390/f13081167.

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Plants and their associated soil microorganisms interact with each other and form complex relationships. The effects of slash-and-burn agriculture and logging on aboveground plants and belowground microorganisms have been extensively studied, but research on plant–microbial interkingdom ecological networks is lacking. In this study, using old growth forest as a control, we used metagenomic data (ITS and 16S rRNA gene amplified sequences) and plant data to obtain interdomain species association patterns for three different soil disturbance types (slash-and-burn, clear cutting and selective cutt

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Prost, Vincent, Stéphane Gazut, and Thomas Brüls. "A zero inflated log-normal model for inference of sparse microbial association networks." PLOS Computational Biology 17, no. 6 (2021): e1009089. http://dx.doi.org/10.1371/journal.pcbi.1009089.

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The advent of high-throughput metagenomic sequencing has prompted the development of efficient taxonomic profiling methods allowing to measure the presence, abundance and phylogeny of organisms in a wide range of environmental samples. Multivariate sequence-derived abundance data further has the potential to enable inference of ecological associations between microbial populations, but several technical issues need to be accounted for, like the compositional nature of the data, its extreme sparsity and overdispersion, as well as the frequent need to operate in under-determined regimes. The eco

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Wan, Xiaoling, Qun Gao, Jianshu Zhao, et al. "Biogeographic patterns of microbial association networks in paddy soil within Eastern China." Soil Biology and Biochemistry 142 (March 2020): 107696. http://dx.doi.org/10.1016/j.soilbio.2019.107696.

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Wu, Linwei, Yunfeng Yang, Si Chen, et al. "Long-term successional dynamics of microbial association networks in anaerobic digestion processes." Water Research 104 (November 2016): 1–10. http://dx.doi.org/10.1016/j.watres.2016.07.072.

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15

Yan, Donghui, Liu Cao, Muqing Zhou, and Hosein Mohimani. "TransDiscovery: Discovering Biotransformation from Human Microbiota by Integrating Metagenomic and Metabolomic Data." Metabolites 12, no. 2 (2022): 119. http://dx.doi.org/10.3390/metabo12020119.

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The human microbiome is a complex community of microorganisms, their enzymes, and the molecules they produce or modify. Recent studies show that imbalances in human microbial ecosystems can cause disease. Our microbiome affects our health through the products of biochemical reactions catalyzed by microbial enzymes (microbial biotransformations). Despite their significance, currently, there are no systematic strategies for identifying these chemical reactions, their substrates and molecular products, and their effects on health and disease. We present TransDiscovery, a computational algorithm t

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Qiu, Mengjia, Xingning Xiao, Yingping Xiao, et al. "Dynamic Changes of Bacterial Communities and Microbial Association Networks in Ready-to-Eat Chicken Meat during Storage." Foods 11, no. 22 (2022): 3733. http://dx.doi.org/10.3390/foods11223733.

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Ready-to-eat (RTE) chicken is a popular food in China, but its lack of food safety due to bacterial contamination remains a concern, and the dynamic changes of microbial association networks during storage are not fully understood. This study investigated the impact of storage time and temperature on bacterial compositions and microbial association networks in RTE chicken using 16S rDNA high-throughput sequencing. The results show that the predominant phyla present in all samples were Proteobacteria and Firmicutes, and the most abundant genera were Weissella, Pseudomonas and Proteus. Increased

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Laccourreye, Paula, Concha Bielza, and Pedro Larrañaga. "Explainable Machine Learning for Longitudinal Multi-Omic Microbiome." Mathematics 10, no. 12 (2022): 1994. http://dx.doi.org/10.3390/math10121994.

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Over the years, research studies have shown there is a key connection between the microbial community in the gut, genes, and immune system. Understanding this association may help discover the cause of complex chronic idiopathic disorders such as inflammatory bowel disease. Even though important efforts have been put into the field, the functions, dynamics, and causation of dysbiosis state performed by the microbial community remains unclear. Machine learning models can help elucidate important connections and relationships between microbes in the human host. Our study aims to extend the curre

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Mousavi, Daniel Cyrus, Aditya Mishra, Yan Jiang, et al. "Abstract LB109: Network analysis of gut microbiome throughout a whole foods based high fiber dietary intervention reveals complex community dynamics in melanoma survivors." Cancer Research 83, no. 8_Supplement (2023): LB109. http://dx.doi.org/10.1158/1538-7445.am2023-lb109.

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Abstract Recent evidence has demonstrated that the gut microbiome modulates response to immune checkpoint blockade (ICB) treatment in melanoma patients. Microbiome modulation via a habitual high-fiber diet was associated with significantly improved progression-free survival (PFS) in melanoma patients on ICB. Previous findings have suggested that this pro-response is associated with known fiber-responsive taxa and Short Chain Fatty Acid (SCFA) producing taxa. However, little is known about the communications responsible for stimulating the aforementioned taxa. To explore community dynamics and

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Parente, Eugenio, Teresa Zotta, and Annamaria Ricciardi. "A review of methods for the inference and experimental confirmation of microbial association networks in cheese." International Journal of Food Microbiology 368 (May 2022): 109618. http://dx.doi.org/10.1016/j.ijfoodmicro.2022.109618.

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Bubier, Jason A., Vivek M. Philip, Christopher Quince, et al. "A Microbe Associated with Sleep Revealed by a Novel Systems Genetic Analysis of the Microbiome in Collaborative Cross Mice." Genetics 214, no. 3 (2020): 719–33. http://dx.doi.org/10.1534/genetics.119.303013.

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The microbiome influences health and disease through complex networks of host genetics, genomics, microbes, and environment. Identifying the mechanisms of these interactions has remained challenging. Systems genetics in laboratory mice (Mus musculus) enables data-driven discovery of biological network components and mechanisms of host–microbial interactions underlying disease phenotypes. To examine the interplay among the whole host genome, transcriptome, and microbiome, we mapped QTL and correlated the abundance of cecal messenger RNA, luminal microflora, physiology, and behavior in a highly

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Xu, Yang, Hongmei Jiang, and Wenxin Jiang. "Extended graphical lasso for multiple interaction networks for high dimensional omics data." PLOS Computational Biology 17, no. 10 (2021): e1008794. http://dx.doi.org/10.1371/journal.pcbi.1008794.

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There has been a spate of interest in association networks in biological and medical research, for example, genetic interaction networks. In this paper, we propose a novel method, the extended joint hub graphical lasso (EDOHA), to estimate multiple related interaction networks for high dimensional omics data across multiple distinct classes. To be specific, we construct a convex penalized log likelihood optimization problem and solve it with an alternating direction method of multipliers (ADMM) algorithm. The proposed method can also be adapted to estimate interaction networks for high dimensi

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Reiman, Derek, Brian T. Layden, and Yang Dai. "MiMeNet: Exploring microbiome-metabolome relationships using neural networks." PLOS Computational Biology 17, no. 5 (2021): e1009021. http://dx.doi.org/10.1371/journal.pcbi.1009021.

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The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational tools to uncover these relationships are just emerging. Here, we present MiMeNet, a neural network framework for modeling microbe-metabolite relationships. Using ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets, we show that MiMeNet more accurately predicts metabolite abundances (mean Spearman correlation coeff

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Chen, Huaihai, Kayan Ma, Yu Huang, et al. "Lower functional redundancy in “narrow” than “broad” functions in global soil metagenomics." SOIL 8, no. 1 (2022): 297–308. http://dx.doi.org/10.5194/soil-8-297-2022.

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Abstract. Understanding the relationship between soil microbial taxonomic compositions and functional profiles is essential for predicting ecosystem functions under various environmental disturbances. However, even though microbial communities are sensitive to disturbance, ecosystem functions remain relatively stable, as soil microbes are likely to be functionally redundant. Microbial functional redundancy may be more associated with “broad” functions carried out by a wide range of microbes than with “narrow” functions in which specific microorganisms specialize. Thus, a comprehensive study to

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Karpe, Avinash V., David J. Beale, and Cuong D. Tran. "Intelligent Biological Networks: Improving Anti-Microbial Resistance Resilience through Nutritional Interventions to Understand Protozoal Gut Infections." Microorganisms 11, no. 7 (2023): 1800. http://dx.doi.org/10.3390/microorganisms11071800.

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Enteric protozoan pathogenic infections significantly contribute to the global burden of gastrointestinal illnesses. Their occurrence is considerable within remote and indigenous communities and regions due to reduced access to clean water and adequate sanitation. The robustness of these pathogens leads to a requirement of harsh treatment methods, such as medicinal drugs or antibiotics. However, in addition to protozoal infection itself, these treatments impact the gut microbiome and create dysbiosis. This often leads to opportunistic pathogen invasion, anti-microbial resistance, or functional

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Reiman, Derek, Ahmed Metwally, Jun Sun, and Yang Dai. "Meta-Signer: Metagenomic Signature Identifier based onrank aggregation of features." F1000Research 10 (March 9, 2021): 194. http://dx.doi.org/10.12688/f1000research.27384.1.

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The advance of metagenomic studies provides the opportunity to identify microbial taxa that are associated with human diseases. Multiple methods exist for the association analysis. However, the results could be inconsistent, presenting challenges in interpreting the host-microbiome interactions. To address this issue, we develop Meta-Signer, a novel Metagenomic Signature Identifier tool based on rank aggregation of features identified from multiple machine learning models including Random Forest, Support Vector Machines, Logistic Regression, and Multi-Layer Perceptron Neural Networks. Meta-Sig

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Chandran, Desirae, Kaitlyn Warren, Daniel McKeone, and Steven D. Hicks. "The Association between Infant Colic and the Multi-Omic Composition of Human Milk." Biomolecules 13, no. 3 (2023): 559. http://dx.doi.org/10.3390/biom13030559.

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Infant colic is a common condition with unclear biologic underpinnings and limited treatment options. We hypothesized that complex molecular networks within human milk (i.e., microbes, micro-ribonucleic acids (miRNAs), cytokines) would contribute to colic risk, while controlling for medical, social, and nutritional variables. This hypothesis was tested in a cohort of 182 breastfed infants, assessed with a modified Infant Colic Scale at 1 month. RNA sequencing was used to interrogate microbial and miRNA features. Luminex assays were used to measure growth factors and cytokines. Milk from mother

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Eissa, Mostafa Essam, Engy Refaat Rashed, and Dalia Essam Eissa. "Dendrogram Analysis and Statistical Examination for Total Microbiological Mesophilic Aerobic Count of Municipal Water Distribution Network System." HighTech and Innovation Journal 3, no. 1 (2022): 28–36. http://dx.doi.org/10.28991/hij-2022-03-01-03.

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The microbiological quality of water for human consumption is a critical safety aspect that should not be overlooked, especially when considering facilities for healthcare and the treatment of ill populations. Thus, the biological stability of water is crucial for the distribution network that delivers potable water to the final users for consumption and other human activities. The present work aimed to study a municipal distribution network system for city water within a healthcare facility. The implementation of the statistical analysis was conducted over long-term data collection, and the c

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Farsijani, Samaneh, Jane Cauley, Peggy Cawthon, et al. "ASSOCIATIONS BETWEEN WALKING SPEED AND GUT MICROBIOME DIVERSITY IN OLDER MEN FROM THE MROS STUDY." Innovation in Aging 7, Supplement_1 (2023): 600–601. http://dx.doi.org/10.1093/geroni/igad104.1963.

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Abstract While gut dysbiosis has been linked to frailty in aging, its association with early mobility impairments is unclear. Here, our primary goal was to determine the cross-sectional associations between walking speed and gut microbiome in 740 older men (84±4y) from MrOS with available stool samples and 400m walking speed measured in 2014–16. We also analyzed the retrospective longitudinal associations between changes in 6-meter walking speed (from 2005-06 to 2014-16) and gut microbiome composition among participants with available data (702/740). The gut microbiome composition was determin

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Bertsch, Annalisse, Denis Roy, and Gisèle LaPointe. "Enhanced Exopolysaccharide Production by Lactobacillus rhamnosus in Co-Culture with Saccharomyces cerevisiae." Applied Sciences 9, no. 19 (2019): 4026. http://dx.doi.org/10.3390/app9194026.

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Lactobacillus strains are known to produce exopolysaccharides (EPS) with recognized health benefits (i.e. prebiotic and immunomodulation) but production is limited by low yields. Co-culture has been shown to improve metabolite productivity, particularly bacteriocins and EPS. Although lactic acid bacteria (LAB) and yeasts are found in several fermented products, the molecular mechanisms linked to the microbial interactions and their influences on EPS biosynthesis are unclear. The aim of the present study was to investigate the effect of co-culture on EPS production by three Lactobacillus rhamno

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Liu, Maidi, Yanqing Ye, Jiang Jiang, and Kewei Yang. "MANIEA: a microbial association network inference method based on improved Eclat association rule mining algorithm." Bioinformatics, May 10, 2021. http://dx.doi.org/10.1093/bioinformatics/btab241.

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Abstract Motivation Modeling microbiome systems as complex networks are known as the problem of network inference. Microbial association network inference is of great significance in applications on clinical diagnosis, disease treatment, pathological analysis, etc. However, most current network inference methods focus on mining strong pairwise associations between microorganisms, which is defective in reflecting the comprehensive interactive patterns participated by multiple microorganisms. It is also possible that the microorganisms involved in the generated network are not dominant in the mi

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Deutschmann, Ina Maria, Gipsi Lima-Mendez, Anders K. Krabberød, et al. "Disentangling environmental effects in microbial association networks." Microbiome 9, no. 1 (2021). http://dx.doi.org/10.1186/s40168-021-01141-7.

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Abstract Background Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly

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Lam, Tony J., and Yuzhen Ye. "Meta-analysis of microbiome association networks reveal patterns of dysbiosis in diseased microbiomes." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-22541-1.

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AbstractThe human gut microbiome is composed of a diverse and dynamic population of microbial species which play key roles in modulating host health and physiology. While individual microbial species have been found to be associated with certain disease states, increasing evidence suggests that higher-order microbial interactions may have an equal or greater contribution to host fitness. To better understand microbial community dynamics, we utilize networks to study interactions through a meta-analysis of microbial association networks between healthy and disease gut microbiomes. Taking advant

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Röttjers, Lisa, Doris Vandeputte, Jeroen Raes, and Karoline Faust. "Null-model-based network comparison reveals core associations." ISME Communications 1, no. 1 (2021). http://dx.doi.org/10.1038/s43705-021-00036-w.

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AbstractMicrobial network construction and analysis is an important tool in microbial ecology. Such networks are often constructed from statistically inferred associations and may not represent ecological interactions. Hence, microbial association networks are error prone and do not necessarily reflect true community structure. We have developed anuran, a toolbox for investigation of noisy networks with null models. Such models allow researchers to generate data under the null hypothesis that all associations are random, supporting identification of nonrandom patterns in groups of association

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Peschel, Stefanie, Christian L. Müller, Erika von Mutius, Anne-Laure Boulesteix, and Martin Depner. "NetCoMi: network construction and comparison for microbiome data in R." Briefings in Bioinformatics, December 3, 2020. http://dx.doi.org/10.1093/bib/bbaa290.

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Abstract Motivation Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g. between healthy individuals and patients, identifying network differences between groups is often an integral seco

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Deutschmann, Ina Maria, Anders K. Krabberød, Francisco Latorre, et al. "Disentangling temporal associations in marine microbial networks." Microbiome 11, no. 1 (2023). http://dx.doi.org/10.1186/s40168-023-01523-z.

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Abstract Background Microbial interactions are fundamental for Earth’s ecosystem functioning and biogeochemical cycling. Nevertheless, they are challenging to identify and remain barely known. Omics-based censuses are helpful in predicting microbial interactions through the statistical inference of single (static) association networks. Yet, microbial interactions are dynamic and we have limited knowledge of how they change over time. Here, we investigate the dynamics of microbial associations in a 10-year marine time series in the Mediterranean Sea using an approach inferring a time-resolved (

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Wang, Mengqi, and Qichao Tu. "Effective data filtering is prerequisite for robust microbial association network construction." Frontiers in Microbiology 13 (October 4, 2022). http://dx.doi.org/10.3389/fmicb.2022.1016947.

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Microorganisms do not exist as individual population in the environment. Rather, they form complex assemblages that perform essential ecosystem functions and maintain ecosystem stability. Besides the diversity and composition of microbial communities, deciphering their potential interactions in the form of association networks has attracted many microbiologists and ecologists. Much effort has been made toward the methodological development for constructing microbial association networks. However, microbial profiles suffer dramatically from zero values, which hamper accurate association network

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Xiao, Naijia, Aifen Zhou, Megan L. Kempher, et al. "Disentangling direct from indirect relationships in association networks." Proceedings of the National Academy of Sciences 119, no. 2 (2022). http://dx.doi.org/10.1073/pnas.2109995119.

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Significance Networks are fundamental units for studying complex systems, but reconstructing networks from large-scale experimental data is very challenging in systems biology and microbial ecology, primarily due to the difficulty in unraveling direct and indirect interactions. By tackling several mathematical challenges, this study provides a conceptual framework for disentangling direct and indirect relationships in association networks. The application of iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity) to synthetic, gene expression, and micr

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Faust, Karoline, Gipsi Lima-Mendez, Jean-Sébastien Lerat, et al. "Cross-biome comparison of microbial association networks." Frontiers in Microbiology 6 (October 27, 2015). http://dx.doi.org/10.3389/fmicb.2015.01200.

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Liao, Qingquan, Yuxiang Ye, Zihang Li, Hao Chen, and Linlin Zhuo. "Prediction of miRNA-disease associations in microbes based on graph convolutional networks and autoencoders." Frontiers in Microbiology 14 (April 28, 2023). http://dx.doi.org/10.3389/fmicb.2023.1170559.

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MicroRNAs (miRNAs) are short RNA molecular fragments that regulate gene expression by targeting and inhibiting the expression of specific RNAs. Due to the fact that microRNAs affect many diseases in microbial ecology, it is necessary to predict microRNAs' association with diseases at the microbial level. To this end, we propose a novel model, termed as GCNA-MDA, where dual-autoencoder and graph convolutional network (GCN) are integrated to predict miRNA-disease association. The proposed method leverages autoencoders to extract robust representations of miRNAs and diseases and meantime exploits

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Deutschmann, Ina Maria, Gipsi Lima-Mendez, Anders K. Krabberød, et al. "Correction to: Disentangling environmental effects in microbial association networks." Microbiome 9, no. 1 (2021). http://dx.doi.org/10.1186/s40168-021-01209-4.

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Sazal, Musfiqur, Kalai Mathee, Daniel Ruiz-Perez, Trevor Cickovski, and Giri Narasimhan. "Inferring directional relationships in microbial communities using signed Bayesian networks." BMC Genomics 21, S6 (2020). http://dx.doi.org/10.1186/s12864-020-07065-0.

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Abstract Background Microbe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by a Bayesian Network (BN) about a microbial community. Unlike the previously proposed Co-occurrence Networks (CoNs), BNs are based on conditional dependencies and can help in revealing complex associations. Results In this paper, we propose a

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Junker, Romane, Florence Valence, Michel-Yves Mistou, Stéphane Chaillou, and Helene Chiapello. "Integration of metataxonomic data sets into microbial association networks highlights shared bacterial community dynamics in fermented vegetables." Microbiology Spectrum, May 15, 2024. http://dx.doi.org/10.1128/spectrum.00312-24.

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ABSTRACT The management of food fermentation is still largely based on empirical knowledge, as the dynamics of microbial communities and the underlying metabolic networks that produce safe and nutritious products remain beyond our understanding. Although these closed ecosystems contain relatively few taxa, they have not yet been thoroughly characterized with respect to how their microbial communities interact and dynamically evolve. However, with the increased availability of metataxonomic data sets on different fermented vegetables, it is now possible to gain a comprehensive understanding of

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Li, Kaihang, Kexin Cheng, Haochen Wang, et al. "Disentangling leaf-microbiome interactions in Arabidopsis thaliana by network mapping." Frontiers in Plant Science 13 (October 6, 2022). http://dx.doi.org/10.3389/fpls.2022.996121.

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The leaf microbiota plays a key role in plant development, but a detailed mechanism of microbe-plant relationships remains elusive. Many genome-wide association studies (GWAS) have begun to map leaf microbes, but few have systematically characterized the genetics of how microbes act and interact. Previously, we integrated behavioral ecology and game theory to define four types of microbial interactions – mutualism, antagonism, aggression, and altruism, in a microbial community assembly. Here, we apply network mapping to identify specific plant genes that mediate the topological architecture of

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Chung, Hee Cheol, Irina Gaynanova, and Yang Ni. "Phylogenetically informed Bayesian truncated copula graphical models for microbial association networks." Annals of Applied Statistics 16, no. 4 (2022). http://dx.doi.org/10.1214/21-aoas1598.

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Shi, Yu, Tiantian Ma, Zhongyue Zhang, Zhenlong Xing, and Jianqing Ding. "Foliar herbivory affects the rhizosphere microbial assembly processes and association networks." Rhizosphere, December 2022, 100649. http://dx.doi.org/10.1016/j.rhisph.2022.100649.

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Deutschmann, Ina M., Erwan Delage, Caterina R. Giner, et al. "Disentangling microbial networks across pelagic zones in the tropical and subtropical global ocean." Nature Communications 15, no. 1 (2024). http://dx.doi.org/10.1038/s41467-023-44550-y.

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AbstractMicrobial interactions are vital in maintaining ocean ecosystem function, yet their dynamic nature and complexity remain largely unexplored. Here, we use association networks to investigate possible ecological interactions in the marine microbiome among archaea, bacteria, and picoeukaryotes throughout different depths and geographical regions of the tropical and subtropical global ocean. Our findings reveal that potential microbial interactions change with depth and geographical scale, exhibiting highly heterogeneous distributions. A few potential interactions were global, meaning they

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Escalas, Arthur, Marc Troussellier, Delphine Melayah, et al. "Strong reorganization of multi-domain microbial networks associated with primary producers sedimentation from oxic to anoxic conditions in an hypersaline lake." FEMS Microbiology Ecology 97, no. 12 (2021). http://dx.doi.org/10.1093/femsec/fiab163.

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ABSTRACT Understanding the role of microbial interactions in the functioning of natural systems is often impaired by the levels of complexity they encompass. In this study, we used the relative simplicity of an hypersaline crater lake hosting only microbial organisms (Dziani Dzaha) to provide a detailed analysis of the microbial networks including the three domains of life. We identified two main ecological zones, one euphotic and oxic zone in surface, where two phytoplanktonic organisms produce a very high biomass, and one aphotic and anoxic deeper zone, where this biomass slowly sinks and un

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Wu, Linwei, Xiaoyu Shan, Si Chen, et al. "Progressive Microbial Community Networks with Incremental Organic Loading Rates Underlie Higher Anaerobic Digestion Performance." mSystems 5, no. 1 (2020). http://dx.doi.org/10.1128/msystems.00357-19.

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ABSTRACT Although biotic interactions among members of microbial communities have been conceived to be crucial for community assembly, it remains unclear how changes in environmental conditions affect microbial interaction and consequently system performance. Here, we adopted a random matrix theory-based network analysis to explore microbial interactions in triplicate anaerobic digestion (AD) systems, which is widely applied for organic pollutant treatments. The digesters were operated with incremental organic loading rates (OLRs) from 1.0 g volatile solids (VS)/liter/day to 1.3 g VS/liter/day

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Xing, Jieqi, Yu Shi, Xiaoquan Su, and Shunyao Wu. "Discovering Microbe-disease Associations with Weighted Graph Convolution Networks and Taxonomy Common Tree." Current Bioinformatics 18 (December 1, 2023). http://dx.doi.org/10.2174/0115748936270441231116093650.

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Background:: Microbe-disease associations are integral to understanding complex dis-eases and their screening procedures. Objective:: While numerous computational methods have been developed to detect these associa-tions, their performance remains limited due to inadequate utilization of weighted inherent similari-ties and microbial taxonomy hierarchy. To address this limitation, we have introduced WTHMDA (weighted taxonomic heterogeneous network-based microbe-disease association), a novel deep learning framework. Methods:: WTHMDA combines a weighted graph convolution network and the microbial

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Yang, Chao, Wei Tang, Junqi Sun, et al. "Weeds in the Alfalfa Field Decrease Rhizosphere Microbial Diversity and Association Networks in the North China Plain." Frontiers in Microbiology 13 (March 17, 2022). http://dx.doi.org/10.3389/fmicb.2022.840774.

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The competition between weeds and crops for soil nutrients is affected by soil microorganisms, which drive diverse ecological processes and are critical in maintaining the stability of agroecosystems. However, the effects of plant species identity, particularly between forage and weed, on soil microbial diversity, composition, and association are not well understood. Here, we investigate the soil physicochemical properties and bacterial/fungal communities in an agroecosystem with native alfalfa [Medicago stativa (Ms)] and five common weed species (Digitaria sanguinalis, Echinochloa crusgalli,

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