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Journal articles on the topic 'Microbial association networks'
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Lo, Chieh, and Radu Marculescu. "MPLasso: Inferring microbial association networks using prior microbial knowledge." PLOS Computational Biology 13, no. 12 (December 27, 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 meconium samples from c-section and vaginally delivery modes. Interestingly, bacterial networks from the c-section-delivered meconium samples tended to have a bigger diameter but fewer correlations, whereas the vaginally delivered meconium networks were smaller and with a higher number of correlations. Even more, the networks were similar in the delivery mode, even between countries, at the genera level. The c-section networks suggest incomplete colonization or important lack of bacteria, promoting the susceptibility of the network to receive new members, beneficial or pathogens. These results suggest that the network analysis contributes to the knowledge of microbiota composition, identifying microbial associations, despite the differences between the environment and country habits, and obtaining a better understanding of microbial gut colonization.
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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 (January 30, 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 degradation process. Derived co-occurrence networks were found to be of a modular structure, with modules being associated with specific spatial locations and metabolic activity along the contamination plume. Association rules identify species that never occur without another, hence identifying potential one-sided cross-feeding relationships. We find that hub nodes in the rule network appearing in many rules as targets qualify as potential keystone species that catalyze critical transformation steps and are able to interact with varying partners. By contrasting analysis based on data derived from bulk samples and individual soil particles, we highlight the importance of spatial sample resolution. While individual inferred interactions are hypothetical in nature, requiring experimental verification, the observed global network patterns provide a unique first glimpse at the complex interaction networks at work in the microbial world.
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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 communities in the human body, as well as their functionalities. In this study, we analyzed 156 gut metagenomics samples from patients with colorectal cancer (CRC) and adenoma, as well as that from healthy controls. We estimated the abundance of microbial enzymes using the HMP Unified Metabolic Analysis Network method and identified the differentially abundant enzymes between CRCs and controls. We constructed enzymatic association networks using the extended local similarity analysis algorithm. We identified CRC-associated enzymic changes by analyzing the topological features of the enzymatic association networks, including the clustering coefficient, the betweenness centrality, and the closeness centrality of network nodes. The network topology of enzymatic association network exhibited a difference between the healthy and the CRC environments. The ABC (ATP binding cassette) transporter and small subunit ribosomal protein S19 enzymes, had the highest clustering coefficient in the healthy enzymatic networks. In contrast, the Adenosylhomocysteinase enzyme had the highest clustering coefficient in the CRC enzymatic networks. These enzymic and metabolic differences may serve as risk predictors for CRCs and are worthy of further research.
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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 sequencing of arctic soil samples. The CoNet app is available at: http://apps.cytoscape.org/apps/conet.
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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 sequencing of arctic soil samples. The CoNet app is available at: http://apps.cytoscape.org/apps/conet.
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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 (April 27, 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 with microbiome studies. This not only necessitates the need for categorical networks, but also integrated and bi-partite networks. Multiple options of network inference algorithms further add to the efforts required for performing correlation-based microbiome interaction studies. We present MetagenoNets, a web-based application, which accepts multi-environment microbial abundance as well as functional profiles, intelligently segregates ‘continuous and categorical’ meta-data and allows inference as well as visualization of categorical, integrated (inter-omic) and bi-partite networks. Modular structure of MetagenoNets ensures logical flow of analysis (inference, integration, exploration and comparison) in an intuitive and interactive personalized dashboard driven framework. Dynamic choice of filtration, normalization, data transformation and correlation algorithms ensures, that end-users get a one-stop solution for microbial network analysis. MetagenoNets is freely available at https://web.rniapps.net/metagenonets.
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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 seasonal marine microbial association networks have characters of complex networks, the same environmental factor influences different species in the four seasons, and the correlative relationships are stronger between OTUs (taxa) than with environmental factors in the four seasons detecting community.
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Poudel, R., A. Jumpponen, D. C. Schlatter, T. C. Paulitz, B. B. McSpadden Gardener, L. L. Kinkel, and K. A. Garrett. "Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management." Phytopathology® 106, no. 10 (October 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 taxa with direct or indirect associations with that node. “Pathogen-focused analysis” identifies taxa with direct or indirect associations with taxa known a priori as pathogens. “Disease-focused analysis” identifies taxa associated with disease. Positive direct or indirect associations with desirable outcomes, or negative associations with undesirable outcomes, indicate candidate taxa. Network analysis provides characterization not only of taxa with direct associations with important outcomes such as disease suppression, biofertilization, or expression of plant host resistance, but also taxa with indirect associations via their association with other key taxa. We illustrate the interpretation of network structure with analyses of microbiomes in the oak phyllosphere, and in wheat rhizosphere and bulk soil associated with the presence or absence of infection by Rhizoctonia solani.
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Avila-Jimenez, Maria-Luisa, Gavin Burns, Zhili He, Jizhong Zhou, Andrew Hodson, Jose-Luis Avila-Jimenez, and David Pearce. "Functional Associations and Resilience in Microbial Communities." Microorganisms 8, no. 6 (June 24, 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 across bacterial networks and that this functional distribution was also even across different geographic locations. For example, on average 15% of the functional genes were related to carbon cycling across all bacterial networks, slightly over 21% of the genes were stress-related and only 0.5% of the genes were linked to carbon degradation functions. In such a distribution, each bacterial network includes all of the functional groups distributed following the same proportions. However, the total number of functional genes that is included in each bacterial network differs, with some clusters including many more genes than others. We found that the proportion of times a specific gene must occur to be linked to a specific cluster is 8%, meaning the relationship between the total number of genes in the cluster and the number of genes per function follows a linear pattern: smaller clusters require a gene to appear less frequently to get fixed within the cluster, while larger clusters require higher gene frequencies. We suggest that this mechanism of functional association between equally rare or equally abundant genes could have implications for ecological resilience, as non-dominant genes also associate in fully functioning ecological networks, potentially suggesting that there are always pre-existing functional networks available to exploit new ecological niches (where they can become dominant) as they emerge; for example, in the case of rapid or sudden environmental change. Furthermore, this pattern did not correlate with taxonomic distribution, suggesting that bacteria associate based on functionality and this is independent of its taxonomic position. Our analyses based on ecological networks also showed no clear evidence of recent environmental impact on polar marine microbial communities at the functional level, unless all communities analyzed have changed exactly in the same direction and intensity, which is unlikely given we are comparing areas changing at different rates.
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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 (July 23, 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 cutting) in a tropical rainforest based on interdomain ecological network (IDEN) analysis. Results showed that the soil bacterial–fungal and plant–microbe ecological networks had different topological properties among the three forest disturbance types compared to old growth forest. More nodes, links, higher modularity and negative proportion were found in the selective cutting stand, indicating higher stability with increasing antagonistic relationships and niche differentiation. However, the area of slash-and-burn forest yield opposite results. Network module analysis indicated that different keystone species were found in the four forest types, suggesting alternative stable states among them. Different plant species had more preferential associations with specific fungal taxa than bacterial taxa at the genus level and plant–microbe associations lagged behind bacterial–fungal associations. Overall, compared with old growth forests, the bacterial–fungal and plant–microbe ecological networks in the slash-and-burn and clear cutting stands were simpler, while the network in the selective cutting stand was more complex. Understanding the relationships between aboveground plants and belowground microorganisms under differing disturbance patterns in natural ecosystems will help in better understanding the surrounding ecosystem functions of ecological networks.
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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 (June 18, 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 ecological network reconstruction problem is frequently cast into the paradigm of Gaussian Graphical Models (GGMs) for which efficient structure inference algorithms are available, like the graphical lasso and neighborhood selection. Unfortunately, GGMs or variants thereof can not properly account for the extremely sparse patterns occurring in real-world metagenomic taxonomic profiles. In particular, structural zeros (as opposed to sampling zeros) corresponding to true absences of biological signals fail to be properly handled by most statistical methods. We present here a zero-inflated log-normal graphical model (available at https://github.com/vincentprost/Zi-LN) specifically aimed at handling such “biological” zeros, and demonstrate significant performance gains over state-of-the-art statistical methods for the inference of microbial association networks, with most notable gains obtained when analyzing taxonomic profiles displaying sparsity levels on par with real-world metagenomic datasets.
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Wan, Xiaoling, Qun Gao, Jianshu Zhao, Jiajie Feng, Joy D. van Nostrand, Yunfeng Yang, and Jizhong Zhou. "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, Mengxin Zhao, Zhenwei Zhu, Sihang Yang, Yuanyuan Qu, 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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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 (January 26, 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 that integrates molecular networks (connecting related molecules with similar mass spectra), association networks (connecting co-occurring molecules and microbes) and knowledge bases of microbial enzymes to discover microbial biotransformations, their substrates, and their products. After searching the metabolomics and metagenomics data from the American Gut Project and the Global Foodomic Project, TranDiscovery identified 17 potentially novel biotransformations from the human gut microbiome, along with the corresponding microbial species, substrates, and products.
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Qiu, Mengjia, Xingning Xiao, Yingping Xiao, Jiele Ma, Hua Yang, Han Jiang, Qingli Dong, and Wen Wang. "Dynamic Changes of Bacterial Communities and Microbial Association Networks in Ready-to-Eat Chicken Meat during Storage." Foods 11, no. 22 (November 21, 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 storage time and temperature decreased the richness and diversity of the microorganisms of the bacterial communities. Higher storage temperatures impacted the bacterial community composition more significantly. Microbial interaction analyses showed 22 positive and 6 negative interactions at 4 °C, 30 positive and 12 negative interactions at 8 °C and 44 positive and 45 negative interactions at 22 °C, indicating an increase in the complexity of interaction networks with an increase in the storage temperature. Enterobacter dominated the interactions during storage at 4 and 22 °C, and Pseudomonas did so at 22 °C. Moreover, interactions between pathogenic and/or spoilage bacteria, such as those between Pseudomonas fragi and Weissella viridescens, Enterobacter unclassified and Proteus unclassified, or those between Enterobacteriaceae unclassified and W.viridescens, were observed. This study provides insight into the process involved in RTE meat spoilage and can aid in improving the quality and safety of RTE meat products to reduce outbreaks of foodborne illness.
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Laccourreye, Paula, Concha Bielza, and Pedro Larrañaga. "Explainable Machine Learning for Longitudinal Multi-Omic Microbiome." Mathematics 10, no. 12 (June 9, 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 current knowledge of associations between the human microbiome and health and disease through the application of dynamic Bayesian networks to describe the temporal variation of the gut microbiota and dynamic relationships between taxonomic entities and clinical variables. We develop a set of preprocessing steps to clean, filter, select, integrate, and model informative metagenomics, metatranscriptomics, and metabolomics longitudinal data from the Human Microbiome Project. This study accomplishes novel network models with satisfactory predictive performance (accuracy = 0.648) for each inflammatory bowel disease state, validating Bayesian networks as a framework for developing interpretable models to help understand the basic ways the different biological entities (taxa, genes, metabolites) interact with each other in a given environment (human gut) over time. These findings can serve as a starting point to advance the discovery of novel therapeutic approaches and new biomarkers for precision medicine.
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Mousavi, Daniel Cyrus, Aditya Mishra, Yan Jiang, Tessa M. Kus, Erma Levy, Marco Montalvo, Nadim Ajami, Jennifer Wargo, Carrie MacDougall, and Jennifer McQuade McQuade. "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 (April 14, 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 identify potential keystone communicating taxa, we conducted microbial association network analysis throughout a high-fiber dietary intervention (HFDI) in melanoma survivors. Ten patients were enrolled to the HFDI study and were provided with whole-food-based fiber-enriched meals for the duration of six weeks. Fecal samples were collected longitudinally, and whole genome sequencing (WGS) of the fecal microbiome was used to calculate microbiome composition profiles. OTU abundance data was then used to construct, analyze, and compare association networks across timepoints via the R package NetCoMi. Overall changes in community dynamics were assessed via changes in global network properties, and significant taxa were identified quantitatively via differences in calculated centrality measures as well as visually by NetCoMi’s selection of hubs, or specific keystone taxa. Network analysis across timepoints demonstrated a general increase in connectivity by both quantitative and visual comparison throughout the HFDI. Analysis via multiple association statistics revealed a general rise in several measures of centrality of many known fiber- responsive and SCFA-producing taxa, with many consistently becoming hub taxa. Consensus networks generated by overlapping several networks generated from different association statistics revealed a consistent increase in centrality in two particular species: Ruminococcus bromii and Dorea longicatena. Analysis of networks constructed from only differentially associated taxa revealed similar results, with R. bromii and D. longicatena having numerous changes in associations with fiber-responsive taxa. Increases in general connectivity measures indicate that a HFDI prompts the gut microbiome to become a more interconnected and dynamic community over time. This, in conjunction with an overall increase in centrality in known fiber-responsive and SCFA-producing taxa, reflects an overall pro-response to the HFDI. Networks constructed via different association statistics yield varied results, with many identifying different taxa as hubs. Despite this heterogeneity, network analysis consistently identified D. longicatena and R. bromii as potential keystone species with an important role in communicating with fiber-responsive taxa. Further analyses are needed to characterize communications between keystone species and SCFA-producing taxa. Citation Format: Daniel Cyrus Mousavi, Aditya Mishra, Yan Jiang, Tessa M. Kus, Erma Levy, Marco Montalvo, Nadim Ajami, Jennifer Wargo, Carrie MacDougall, Jennifer McQuade McQuade. Network analysis of gut microbiome throughout a whole foods based high fiber dietary intervention reveals complex community dynamics in melanoma survivors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB109.
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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, James Campbell, Yanjiao Zhou, Tatiana Vishnivetskaya, Suman Duvvuru, 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 (January 2, 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 diverse Collaborative Cross breeding population. One such relationship, regulated by a variant on chromosome 7, was the association of Odoribacter (Bacteroidales) abundance and sleep phenotypes. In a test of this association in the BKS.Cg-Dock7m +/+ Leprdb/J mouse model of obesity and diabetes, known to have abnormal sleep and colonization by Odoribacter, treatment with antibiotics altered sleep in a genotype-dependent fashion. The many other relationships extracted from this study can be used to interrogate other diseases, microbes, and mechanisms.
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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 (October 20, 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 dimensional compositional data such as microbial interaction networks. The performance of the proposed method in the simulated studies shows that EDOHA has remarkable advantages in recognizing class-specific hubs than the existing comparable methods. We also present three applications of real datasets. Biological interpretations of our results confirm those of previous studies and offer a more comprehensive understanding of the underlying mechanism in disease.
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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 (May 17, 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 coefficients increase from 0.108 to 0.309, 0.276 to 0.457, and -0.272 to 0.264) and identifies more well-predicted metabolites (increase in the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29) compared to state-of-art linear models for individual metabolite predictions. Additionally, we demonstrate that MiMeNet can group microbes and metabolites with similar interaction patterns and functions to illuminate the underlying structure of the microbe-metabolite interaction network, which could potentially shed light on uncharacterized metabolites through “Guilt by Association”. Our results demonstrated that MiMeNet is a powerful tool to provide insights into the causes of metabolic dysregulation in disease, facilitating future hypothesis generation at the interface of the microbiome and metabolomics.
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Chen, Huaihai, Kayan Ma, Yu Huang, Qi Fu, Yingbo Qiu, Jiajiang Lin, Christopher W. Schadt, and Hao Chen. "Lower functional redundancy in “narrow” than “broad” functions in global soil metagenomics." SOIL 8, no. 1 (April 8, 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 evaluate how microbial taxonomic compositions correlate with broad and narrow functional profiles is necessary. Here, we evaluated soil metagenomes worldwide to assess whether functional and taxonomic diversities differ significantly between the five broad and the five narrow functions that we chose. Our results revealed that, compared with the five broad functions, soil microbes capable of performing the five narrow functions were more taxonomically diverse, and thus their functional diversity was more dependent on taxonomic diversity, implying lower levels of functional redundancy in narrow functions. Co-occurrence networks indicated that microorganisms conducting broad functions were positively related, but microbes specializing in narrow functions were interacting mostly negatively. Our study provides strong evidence to support our hypothesis that functional redundancy is significantly different between broad and narrow functions in soil microbes, as the association of functional diversity with taxonomy was greater in the five narrow than in the five broad functions.
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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 (July 13, 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 gastrointestinal disorders, such as irritable bowel syndrome. Moreover, these impacts do not remain confined to the gut and are reflected across the gut–brain, gut–liver, and gut–lung axes, among others. Therefore, apart from medicinal treatment, nutritional supplementation is also a key aspect of providing recovery from this dysbiosis. Future proteins, prebiotics, probiotics, synbiotics, and food formulations offer a good solution to remedy this dysbiosis. Furthermore, nutritional supplementation also helps to build resilience against opportunistic pathogens and potential future infections and disorders that may arise due to the dysbiosis. Systems biology techniques have shown to be highly effective tools to understand the biochemistry of these processes. Systems biology techniques characterize the fundamental host–pathogen interaction biochemical pathways at various infection and recovery stages. This same mechanism also allows the impact of the abovementioned treatment methods of gut microbiome remediation to be tracked. This manuscript discusses system biology approaches, analytical techniques, and interaction and association networks, to understand (1) infection mechanisms and current global status; (2) cross-organ impacts of dysbiosis, particularly within the gut–liver and gut–lung axes; and (3) nutritional interventions. This study highlights the impact of anti-microbial resistance and multi-drug resistance from the perspective of protozoal infections. It also highlights the role of nutritional interventions to add resilience against the chronic problems caused by these phenomena.
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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-Signer generates ranked taxa lists by training individual machine learning models over multiple training partitions and aggregates the ranked lists into a single list by an optimization procedure to represent the most informative and robust microbial features. A User will receive speedy assessment on the predictive performance of each ma-chine learning model using different numbers of the ranked features and determine the final models to be used for evaluation on external datasets. Meta-Signer is user-friendly and customizable, allowing users to explore their datasets quickly and efficiently.
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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 (March 18, 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 mothers of infants with colic (n = 28) displayed higher levels of Staphylococcus (adj. p = 0.038, d = 0.30), miR-224-3p (adj. p = 0.023, d = 0.33), miR-125b-5p (adj. p = 0.028, d = 0.29), let-7a-5p (adj. p = 0.028, d = 0.27), and miR-205-5p (adj. p = 0.029, d = 0.26) compared to milk from non-colic mother–infant dyads (n = 154). Colic symptom severity was directly associated with milk hepatocyte growth factor levels (R = 0.21, p = 0.025). A regression model involving let-7a-5p, miR-29a-3p, and Lactobacillus accurately modeled colic risk (X2 = 16.7, p = 0.001). Molecular factors within human milk may impact colic risk, and provide support for a dysbiotic/inflammatory model of colic pathophysiology.
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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 (March 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 comparative study for the microbiological count of the water samples - from different points-of-use was assessed using the non-parametric analysis of the Kruskal-Wallis test. The comparative study involved a preliminary general one-way Analysis of Variance (ANOVA) followed by ad-hoc pairwise comparison. The statistical study involved a correlation matrix and a dendrogram to elucidate the level of association between different sections in the network. The ports C4 and C13 were at the trough in the microbiological count, in contrast to C13, which showed the highest level of the average microbial density. Despite a low to moderate level of correlation between the datasets of the water network, the tree diagram (dendrogram) analysis showed remarkable clustering. Use points could be grouped into three dense groups based on abrupt cuts in the similarity value. The study was useful in the analysis of the pattern and behavior of the microbial quality in a distribution water network in a specific area of the study. This work in turn would help in investigating the areas of improvement and defect spotting, in addition to assessing the biological stability of the water distribution system. The study could be extended to cover other different processed water networks, such as distilled, deionized, and purified water, as well as Water-For-Injection (WFI). Doi: 10.28991/HIJ-2022-03-01-03 Full Text: PDF
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Farsijani, Samaneh, Jane Cauley, Peggy Cawthon, Lisa Langsetmo, Eric Orwoll, Anne Newman, and Deborah Kado. "ASSOCIATIONS BETWEEN WALKING SPEED AND GUT MICROBIOME DIVERSITY IN OLDER MEN FROM THE MROS STUDY." Innovation in Aging 7, Supplement_1 (December 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 determined by 16S sequencing (DADA2 and SILVA). We examined diversity, taxa abundance (by ANCOM-BC), and performed network analysis (by NetCoMi) to uncover microbial communities interactions by walking speed levels. Higher walking speed (m/s) was associated with greater microbiome Shannon α-diversity (R=0.11; P=0.004). Decline in walking speed was associated with lower Shannon α-diversity (R=0.07; P=0.054). Faster walking speed and less decline in walking speed were associated with higher abundance of genus-level bacteria that produce short-chain fatty acids, and possess anti-inflammatory properties, including Paraprevotella, Fusicatenibacter, and Alistipes, adjusting for age, race, site, education, health, marital status, weight, height, physical activity, batch, medications, energy, and fiber intake (P< 0.05). The gut microbiome networks of participants in the first vs. last quartile of walking speed (≤0.9 vs. ≥1.2 m/s) exhibited distinct characteristics, including different cluster numbers, hubs, and centrality measures (P< 0.05). Faster walking speed and its less decline were associated with higher gut microbiome diversity, suggesting potential role of microbiome in preserving mobility in aging.
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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 (September 26, 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 rhamnosus strains (ATCC 9595, R0011, and RW-9595M) in association with Saccharomyces cerevisiae. Fermentation, in both mono and co-culture, was carried out and the expression of key LAB genes was monitored. After 48 h, results revealed that EPS production was enhanced by 39%, 49%, and 42% in co-culture for R0011, ATCC 9595, and RW-9595M, respectively. Each strain showed distinctive gene expression profiles. For a higher EPS production, higher EPS operon expression levels were observed for RW-9595M in co-culture. The construction of gene co-expression networks revealed common correlations between the expression of genes related to the EPS operons, sugar metabolism, and stress during EPS production and microbial growth for the three strains. Our findings provide insight into the positive influence of inter-kingdom interactions in stimulating EPS biosynthesis, representing progress toward the development of a bio-ingredient with broad industrial applications.
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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 microbiome due to the mere focus on the strength of pairwise associations. Some scholars tried to mine comprehensive microbial associations by association rule mining methods, but the adopted algorithms are relatively basic and have severe limitations such as low calculation efficiency, lacking the ability of mining negative correlations and high redundancy in results, making it difficult to mine high-quality microbial association rules and accurately infer microbial association networks. Results We proposed a microbial association network inference method ‘MANIEA’ based on the improved Eclat algorithm for mining positive and negative microbial association rules. We also proposed a new method for transforming association rules into microbial association networks, which can effectively demonstrate the co-occurrence and causal correlations in association rules. An experiment was conducted on three authentic microbial abundance datasets to compare the ‘MANIEA’ with currently popular network inference methods, which demonstrated that the proposed ‘MANIEA’ show advantages in aspects of correlation forms, computation efficiency, adjustability and network characteristics. Availability and implementation The algorithms and data are available at: https://github.com/MaidiL/MANIEA.
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Deutschmann, Ina Maria, Gipsi Lima-Mendez, Anders K. Krabberød, Jeroen Raes, Sergio M. Vallina, Karoline Faust, and Ramiro Logares. "Disentangling environmental effects in microbial association networks." Microbiome 9, no. 1 (November 26, 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 if it is due to environmental selection or not. Results We present EnDED (environmentally driven edge detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally driven. The four approaches are sign pattern, overlap, interaction information, and data processing inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e., environmentally driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally driven edges—87% sign pattern and overlap, 67% interaction information, and 44% data processing inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally driven, while individual methods predicted 24.8% (data processing inequality), 25.7% (interaction information), and up to 84.6% (sign pattern as well as overlap). The fraction of environmentally driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors. Conclusions To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses.
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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 (October 19, 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 advantage of the large number of metagenomes derived from healthy individuals and patients with various diseases, together with recent advances in network inference that can deal with sparse compositional data, we inferred microbial association networks based on co-occurrence of gut microbial species and made the networks publicly available as a resource (GitHub repository named GutNet). Through our meta-analysis of inferred networks, we were able to identify network-associated features that help stratify between healthy and disease states such as the differentiation of various bacterial phyla and enrichment of Proteobacteria interactions in diseased networks. Additionally, our findings show that the contributions of taxa in microbial associations are disproportionate to their abundances and that rarer taxa of microbial species play an integral part in shaping dynamics of microbial community interactions. Network-based meta-analysis revealed valuable insights into microbial community dynamics between healthy and disease phenotypes. We anticipate that the healthy and diseased microbiome association networks we inferred will become an important resource for human-related microbiome research.
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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 (July 16, 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 networks. This toolbox compares multiple networks to identify conserved subsets (core association networks, CANs) and other network properties that are shared across all networks. We apply anuran to a time series of fecal samples from 20 women to demonstrate the existence of CANs in a subset of the sampled individuals. Moreover, we use data from the Global Sponge Project to demonstrate that orders of sponges have a larger CAN than expected at random. In conclusion, this toolbox is a resource for investigators wanting to compare microbial networks across conditions, time series, gradients, or hosts.
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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 secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analysing and comparing microbial association networks from high-throughput sequencing data. Results Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analysing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMi’s wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from children’s rooms between samples from two study centers (Ulm and Munich). Availability R scripts used for producing the examples shown in this manuscript are provided as supplementary data. The NetCoMi package, together with a tutorial, is available at https://github.com/stefpeschel/NetCoMi. Contact Tel:+49 89 3187 43258; stefanie.peschel@mail.de Supplementary information Supplementary data are available at Briefings in Bioinformatics online.
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Deutschmann, Ina Maria, Anders K. Krabberød, Francisco Latorre, Erwan Delage, Cèlia Marrasé, Vanessa Balagué, Josep M. Gasol, et al. "Disentangling temporal associations in marine microbial networks." Microbiome 11, no. 1 (April 21, 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 (temporal) network from a single static network. Results A single static network including microbial eukaryotes and bacteria was built using metabarcoding data derived from 120 monthly samples. For the decade, we aimed to identify persistent, seasonal, and temporary microbial associations by determining a temporal network that captures the interactome of each individual sample. We found that the temporal network appears to follow an annual cycle, collapsing, and reassembling when transiting between colder and warmer waters. We observed higher association repeatability in colder than in warmer months. Only 16 associations could be validated using observations reported in literature, underlining our knowledge gap in marine microbial ecological interactions. Conclusions Our results indicate that marine microbial associations follow recurrent temporal dynamics in temperate zones, which need to be accounted for to better understand the functioning of the ocean microbiome. The constructed marine temporal network may serve as a resource for testing season-specific microbial interaction hypotheses. The applied approach can be transferred to microbiome studies in other ecosystems.
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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 construction. In this study, we investigated the effects of zero-value issues associated with microbial association network construction. Using the TARA Oceans microbial profile as an example, different zero-value-treatment approaches were comparatively investigated using different correlation methods. The results suggested dramatic variations of correlation coefficient values for differently treated microbial profiles. Most specifically, correlation coefficients among less frequent microbial taxa were more affected, whichever method was used. Negative correlation coefficients were more problematic and sensitive to network construction, as many of them were inferred from low-overlapped microbial taxa. Consequently, microbial association networks were greatly differed. Among various approaches, we recommend sequential calculation of correlation coefficients for microbial taxa pairs by excluding paired zero values. Filling missing values with pseudo-values is not recommended. As microbial association network analyses have become a widely used technique in the field of microbial ecology and environmental science, we urge cautions be made to critically consider the zero-value issues in microbial data.
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Xiao, Naijia, Aifen Zhou, Megan L. Kempher, Benjamin Y. Zhou, Zhou Jason Shi, Mengting Yuan, Xue Guo, et al. "Disentangling direct from indirect relationships in association networks." Proceedings of the National Academy of Sciences 119, no. 2 (January 6, 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 microbial community data demonstrates that it is a powerful, robust, and reliable tool for network inference. The framework developed here will greatly enhance our capability to discern network interactions in various complex systems and allow scientists to address research questions that could not be approached previously.
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Faust, Karoline, Gipsi Lima-Mendez, Jean-Sébastien Lerat, Jarupon F. Sathirapongsasuti, Rob Knight, Curtis Huttenhower, Tom Lenaerts, and Jeroen Raes. "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 GCN to capture the topological information of miRNA-disease networks. To alleviate the impact of insufficient information for the original data, the association similarity and feature similarity data are combined to calculate a more complete initial basic vector of nodes. The experimental results on the benchmark datasets demonstrate that compared with the existing representative methods, the proposed method has achieved the superior performance and its precision reaches up to 0.8982. These results demonstrate that the proposed method can serve as a tool for exploring miRNA-disease associations in microbial environments.
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Deutschmann, Ina Maria, Gipsi Lima-Mendez, Anders K. Krabberød, Jeroen Raes, Sergio M. Vallina, Karoline Faust, and Ramiro Logares. "Correction to: Disentangling environmental effects in microbial association networks." Microbiome 9, no. 1 (December 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 (December 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 way of combining a BN and a CoN to construct a signed Bayesian Network (sBN). We report a surprising association between directed edges in signed BNs and known colonization orders. Conclusions BNs are powerful tools for community analysis and extracting influences and colonization patterns, even though the analysis only uses an abundance matrix with no temporal information. We conclude that directed edges in sBNs when combined with negative correlations are consistent with and strongly suggestive of colonization order.
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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 the microbial relationships that structure plant fermentation. In this study, we applied a network-based approach to the integration of public metataxonomic 16S data sets targeting different fermented vegetables throughout time. Specifically, we aimed to explore, compare, and combine public 16S data sets to identify shared associations between amplicon sequence variants (ASVs) obtained from independent studies. The workflow includes steps for searching and selecting public time-series data sets and constructing association networks of ASVs based on co-abundance metrics. Networks for individual data sets are then integrated into a core network, highlighting significant associations. Microbial communities are identified based on the comparison and clustering of ASV networks using the “stochastic block model” method. When we applied this method to 10 public data sets (including a total of 931 samples) targeting five varieties of vegetables with different sampling times, we found that it was able to shed light on the dynamics of vegetable fermentation by characterizing the processes of community succession among different bacterial assemblages. IMPORTANCE Within the growing body of research on the bacterial communities involved in the fermentation of vegetables, there is particular interest in discovering the species or consortia that drive different fermentation steps. This integrative analysis demonstrates that the reuse and integration of public microbiome data sets can provide new insights into a little-known biotope. Our most important finding is the recurrent but transient appearance, at the beginning of vegetable fermentation, of amplicon sequence variants (ASVs) belonging to Enterobacterales and their associations with ASVs belonging to Lactobacillales . These findings could be applied to the design of new fermented products.
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Li, Kaihang, Kexin Cheng, Haochen Wang, Qi Zhang, Yan Yang, Yi Jin, Xiaoqing He, and Rongling Wu. "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 microbial networks. Analyzing leaf microbiome data from an Arabidopsis GWAS, we identify several heritable hub microbes for leaf microbial communities and detect 140–728 SNPs (Single nucleotide polymorphisms) responsible for emergent properties of microbial network. We reconstruct Bayesian genetic networks from which to identify 22–43 hub genes found to code molecular pathways related to leaf growth, abiotic stress responses, disease resistance and nutrition uptake. A further path analysis visualizes how genetic variants of Arabidopsis affect its fecundity through the internal workings of the leaf microbiome. We find that microbial networks and their genetic control vary along spatiotemporal gradients. Our study provides a new avenue to reveal the “endophenotype” role of microbial networks in linking genotype to end-point phenotypes in plants. Our integrative theory model provides a powerful tool to understand the mechanistic basis of structural-functional relationships within the leaf microbiome and supports the need for future research on plant breeding and synthetic microbial consortia with a specific function.
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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 (December 1, 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, Marta Sebastián, Julie Poulain, Javier Arístegui, Carlos M. Duarte, et al. "Disentangling microbial networks across pelagic zones in the tropical and subtropical global ocean." Nature Communications 15, no. 1 (January 2, 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 occurred across regions at the same depth, while 11-36% were regional within specific depths. The bathypelagic zone had the lowest proportion of global associations, and regional associations increased with depth. Moreover, we observed that most surface water associations do not persist in deeper ocean layers despite microbial vertical dispersal. Our work contributes to a deeper understanding of the tropical and subtropical global ocean interactome, which is essential for addressing the challenges posed by global change.
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Escalas, Arthur, Marc Troussellier, Delphine Melayah, Maxime Bruto, Sébastien Nicolas, Cécile Bernard, Magali Ader, Christophe Leboulanger, Hélène Agogué, and Mylène Hugoni. "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 (December 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 undergoes anaerobic degradation. We highlighted strong differences in the structure of microbial communities from the two zones and between the microbial consortia associated with the two primary producers. Primary producers sedimentation was associated with a major reorganization of the microbial network at several levels: global properties, modules composition, nodes and links characteristics. We evidenced the potential dependency of Woesearchaeota to the primary producers’ exudates in the surface zone, and their disappearance in the deeper anoxic zone, along with the restructuration of the networks in the anoxic zone toward the decomposition of the organic matter. Altogether, we provided an in-depth analysis of microbial association network and highlighted putative changes in microbial interactions supporting the functioning of the two ecological zones in this unique ecosystem.
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Wu, Linwei, Xiaoyu Shan, Si Chen, Qiuting Zhang, Qi Qi, Ziyan Qin, Huaqun Yin, Jizhong Zhou, Qiang He, and Yunfeng Yang. "Progressive Microbial Community Networks with Incremental Organic Loading Rates Underlie Higher Anaerobic Digestion Performance." mSystems 5, no. 1 (January 7, 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 and then to 1.5 g VS/liter/day, which increased VS removal and methane production proportionally. Higher resource availability led to networks with higher connectivity and shorter harmonic geodesic distance, suggestive of more intense microbial interactions and quicker responses to environmental changes. Strikingly, a number of topological properties of microbial network showed significant (P < 0.05) correlation with AD performance (i.e., methane production, biogas production, and VS removal). When controlling for environmental parameters (e.g., total ammonia, pH, and the VS load), node connectivity, especially that of the methanogenic archaeal network, still correlated with AD performance. Last, we identified the Methanothermus, Methanobacterium, Chlorobium, and Haloarcula taxa and an unclassified Thaumarchaeota taxon as keystone nodes of the network. IMPORTANCE AD is a biological process widely used for effective waste treatment throughout the world. Biotic interactions among microbes are critical to the assembly and functioning of the microbial community, but the response of microbial interactions to environmental changes and their influence on AD performance are still poorly understood. Using well-replicated time series data of 16S rRNA gene amplicons and functional gene arrays, we constructed random matrix theory-based association networks to characterize potential microbial interactions with incremental OLRs. We demonstrated striking linkage between network topological features of methanogenic archaea and AD functioning independent of environmental parameters. As the intricate balance of multiple microbial functional groups is responsible for methane production, our results suggest that microbial interaction may be an important, previously unrecognized mechanism in determining AD performance.
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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 taxono-my common tree to predict microbe-disease associations effectively. The framework extracts mul-tiple microbe similarities from the taxonomy common tree, facilitating the construction of a mi-crobe-disease heterogeneous interaction network. Utilizing a weighted DeepWalk algorithm, node embeddings in the network incorporate weight information from the similarities. Subsequently, a deep neural network (DNN) model accurately predicts microbe-disease associations based on this interaction network. Results:: Extensive experiments on multiple datasets and case studies demonstrate WTHMDA's su-periority over existing approaches, particularly in predicting unknown associations. Conclusion:: Our proposed method offers a new strategy for discovering microbe-disease linkages, showcasing remarkable performance and enhancing the feasibility of identifying disease risk.
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Yang, Chao, Wei Tang, Junqi Sun, Haipeng Guo, Shusheng Sun, Fuhong Miao, Guofeng Yang, Yiran Zhao, Zengyu Wang, and Juan Sun. "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, Acalypha australis, Portulaca oleracea, and Chenopodium album) in the North China Plain. The five weeds had a lower plant carbon content than Ms. while the opposite was true for plant nitrogen and phosphorus concentrations. The Shannon diversity of bacterial and fungal communities of the five weeds were significantly lower than in Ms. Soil pH and PO43−-P were identified as the most important factors in shaping the relative abundances of bacteria (Sphingomonadaceae) and fungi (Pleosporaceae), respectively. Importantly, the weeds greatly inhibited the growth of pathogenic fungi (Nectriaceae and Pleosporaceae). Bacterial co-occurrence networks depended on specific species, indicating that Ms. harbored co-occurrence networks that were more complex than those in the bacterial communities of other weed groups. Our study examines how soil nutrients and the soil microbial community structure of five weed species changed in an Ms. field. This analysis of the microbial ecological network enhances our understanding of the influence of weeds on the soil microbiome in agroecosystems.