Academic literature on the topic 'Microbial association networks'

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.

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.

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.

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.

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.

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.

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.

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.

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.

La pompe à carbone biologique comprend la production primaire de matière organique dans la zone euphotique, son export vers les profondeurs et sa reminéralisation. Les acteurs les plus fréquemment cités sont les diatomées en raison de leur contribution à la production primaire et à l’export de carbone et les copépodes pour la production de pelotes fécales. Cependant, la pompe biologique est le résultat d'interactions complexes entre organismes plutôt que de leurs actions indépendantes. En outre, bien qu'il ait été montré que la distribution de taille et la composition minérale du phytoplancton en surface ont une influence significative sur l'intensité de l'export de carbone, on ne sait pas si les données méta-omiques peuvent prédire efficacement les processus de la pompe à carbone biologique. Dans cette thèse, je propose d’abord de revisiter l’étude de la pompe à carbone biologique dans l’océan oligotrophe en définissant des états biogéochimiques de l’océan sur la base de la contribution relative de la production primaire, de l’export de carbone et de l’atténuation du flux dans les stations d’échantillonnage Tara Océans. L'analyse des états en termes de composition et d'interactions microbiennes inférées à partir de données de métabarcoding a révélé que les associations plutôt que la composition microbienne semblent caractériser les états de la pompe à carbone biologique. Ensuite, en utilisant les données méta-omiques et environnementales des expéditions Tara Oceans, je propose pour la première fois de prédire ces états biogéochimiques à partir d'abondances biologiques dérivées d'ADN environnemental, dans l'objectif de fournir une liste de biomarqueurs
The biological carbon pump encompasses a series of processes including the primary production of organic matter in the surface ocean, its export to deeper waters and its remineralization. The common highlighted actors are diatoms because of their contribution to primary production and carbon export and copepods for their production of fecal pellets. However, the biological pump is the result of complex interactions among organisms rather than their independent actions. Besides, although size distribution and mineral composition of phytoplankton in surface was shown to significantly influence the strength of carbon export, it is unknown whether meta-omic data can efficiently predict the processes of the biological carbon pump. In this thesis, I first propose to revisit the study of the biological carbon pump in the oligotrophic ocean by defining biogeochemical states of the ocean based on the relative contribution of primary production, carbon export and flux attenuation in Tara Oceans sampling stations. The analysis of the states in terms of microbial composition and interactions inferred from metabarcoding data revealed that variation in associations rather than lineages presence seems to drive the states of the biological carbon pump. Then, by using meta-omics and environmental parameters from the Tara Oceans expeditions, I propose the first study trying to predict biogeochemical states from biological abundances derived from environmental DNA, with the goal of providing a list of biomarkers

With the recent advances in high throughput next-generation sequencing technologies and bioinformatics approach, gut microbiome research, especially in livestock species, has expanded immensely, elucidating the greatest potential to investigate the unacknowledged understanding of rumen microbiota in host physiology at the molecular level. The association of a complex aggregated community of microbes to host metabolism is of great importance due to their crucial participation in metabolic, immunological, and physiological tasks. The knowledge of this sophisticated network of a symbiotic association of gut microbiota to host organisms may lead to novel insights for improving health, enhancing production, and reducing the risk of disease progression in livestock species necessary to meet the demands of the human race. The full picture of microorganisms present in a particular area can be achieved with the help of culture-independent omics-based approaches. The integration of metagenomics, metatranscriptomics, metaproteomics, and meta-metabolomics technologies with systems biology emphasizes the taxonomic composition, identification, functional characterization, gene abundance, metabolic profiling, and phylogenetic information of microbial population along with the underlying mechanism for pathological processes and their involvement as probiotic. The rumen secretions or partially digested feed particles, as well as fecal samples, are generally employed for gut microbiome investigation. The 16S rRNA gene sequencing amplicon-based technology is the most employed technique for microbiome profiling in livestock species to date. The use of software and biological databases in the field of gut microbiome research gives an accurate in-depth analysis of the microbial population greatly.