Comparative metabolomics and microbial communities associated network analysis of black and white horse- sourced koumiss

2022 ◽  
Vol 370 ◽  
pp. 130996
Author(s):  
Yanan Xia ◽  
Erdenebat Oyunsuren ◽  
Yang Yang ◽  
Quan Shuang
Keyword(s):  
Network Analysis ◽  
Microbial Communities ◽  
Black And White ◽  
White Horse ◽  
Comparative Metabolomics

scholarly journals Future Climate Alters Pathogens-Microbiome Co-occurrence Networks in Wheat Straw Residues during Decomposition

Proceedings ◽  
2021 ◽  
Vol 66 (1) ◽  
pp. 22
Author(s):  
Sara Fareed Mohamed Wahdan ◽  
François Buscot ◽  
Witoon Purahong
Keyword(s):  
Network Analysis ◽  
Microbial Communities ◽  
Wheat Straw ◽  
Pathogenic Fungi ◽  
Future Climate ◽  
Plant Residues ◽  
Plant Pathogenic Fungi ◽  
Plant Growth Promoting Bacteria ◽  
Ambient Conditions ◽  
Ecological Functions

The return of plant residues to the ground is used to promote soil carbon sequestration, improve soil structure, reduce evaporation, and help to fix additional carbon dioxide in the soil. The microbial communities with diverse ecological functions that colonize plant residues during decomposition are expected to be highly dynamic. We aimed to characterize microbial communities colonizing wheat straw residues and their ecological functions during the early phase of straw decomposition. The experiment, run in Central Germany, was conducted in a conventional farming system under both ambient conditions and a future climate scenario expected in 50–70 years from now. We used MiSeq illumina sequencing and network analysis of bacterial 16S rRNA and fungal ITS genes. Our results show that future climate alters the dynamics of bacterial and fungal communities during decomposition. We detected various microbial ecological functions within wheat straw residues such as plant growth-promoting bacteria, N-fixing bacteria, saprotrophs, and plant pathogenic fungi. Interestingly, plant pathogenic fungi dominated (~87% of the total sequences) within the wheat residue mycobiome under both ambient and future climate conditions. Therefore, we applied co-occurrence network analysis to predict the potential impacts of climate change on the interaction between pathogenic community and other bacterial and fungal microbiomes. The network under ambient climate consisted of 91 nodes and 129 correlations (edges). The highest numbers of connections were detected for the pathogens Mycosphaerella tassiana and Neosetophoma rosigena. The network under future climate consisted of 100 nodes and 170 correlations. The highest numbers of connections were detected for the pathogens Pseudopithomyces rosae and Gibellulopsis piscis. We conclude that the future climate significantly changes the interactions between plant pathogenic fungi and other microorganisms during the early phrase of decomposition.

scholarly journals A New Method to Correct for Habitat Filtering in Microbial Correlation Networks

2018 ◽  
Author(s):  
Vanessa Brisson ◽  
Jennifer Schmidt ◽  
Trent R. Northen ◽  
John P. Vogel ◽  
Amélie Gaudin
Keyword(s):  
Network Analysis ◽  
Microbial Communities ◽  
Simulated Data ◽  
Correlation Network ◽  
Detection Accuracy ◽  
Biological Interactions ◽  
Host Genotype ◽  
Habitat Filtering ◽  
Data Set ◽  
Correlation Networks

AbstractAmplicon sequencing of 16S, ITS, and 18S regions of microbial genomes is a commonly used first step toward understanding microbial communities of interest for human health, agriculture, and the environment. Correlation network analysis is an emerging tool for investigating the interactions within these microbial communities. However, when data from different habitats (e.g sampling sites, host genotype, etc.) are combined into one analysis, habitat filtering (co-occurrence of microbes due to habitat sampled rather than biological interactions) can induce apparent correlations, resulting in a network dominated by habitat effects and masking correlations of biological interest. We developed an algorithm to correct for habitat filtering effects in microbial correlation network analysis in order to reveal the true underlying microbial correlations. This algorithm was tested on simulated data that was constructed to exhibit habitat filtering. Our algorithm significantly improved correlation detection accuracy for these data compared to Spearman and Pearson correlations. We then used our algorithm to analyze a real data set of 16S-V4 amplicon sequences that was expected to exhibit habitat filtering. Our algorithm was found to effectively reduce habitat effects, enabling the construction of consensus correlation networks from data sets combining multiple related sample habitats.

scholarly journals From Diversity to Complexity: Microbial Networks in Soils

2021 ◽  
Author(s):  
Ksenia Guseva ◽  
Sean Darcy ◽  
Eva Simon ◽  
Lauren V. Alteio ◽  
Alicia Montesinos-Navarro ◽  
...  
Keyword(s):  
Network Analysis ◽  
Microbial Communities ◽  
Microbial Ecology ◽  
Soil Microbial Communities ◽  
Small Scale ◽  
Soil Microbial Ecology ◽  
Test Dataset ◽  
Soil Microbial ◽  
Network Properties ◽  
Plant Animal Interactions

Network analysis has been used for many years in ecological research to analyze organismal associations, for example in food webs, plant-plant or plant-animal interactions. Although network analysis is widely applied in microbial ecology, only recently has it entered the realms of soil microbial ecology, shown by a rapid rise in studies applying co-occurrence analysis to soil microbial communities. While this application offers great potential for deeper insights into the ecological structure of soil microbial ecosystems, it also brings new challenges related to the specific characteristics of soil datasets and the type of ecological questions that can be addressed. In this Perspectives Paper we assess the challenges of applying network analysis to soil microbial ecology due to the small-scale heterogeneity of the soil environment and the nature of soil microbial datasets. We review the different approaches of network construction that are commonly applied to soil microbial datasets and discuss their features and limitations. Using a test dataset of microbial communities from two depths of a forest soil, we demonstrate how different experimental designs and network constructing algorithms affect the structure of the resulting networks, and how this in turn may influence ecological conclusions. We will also reveal how assumptions of the construction method, methods of preparing the dataset, an definitions of thresholds affect the network structure. Finally, we discuss the particular questions in soil microbial ecology that can be approached by analyzing and interpreting specific network properties. Targeting these network properties in a meaningful way will allow applying this technique not in merely descriptive, but in hypothesis-driven research.

scholarly journals Trait-based life-history strategies explain succession scenario for complex bacterial communities under varying disturbance

2019 ◽  
Author(s):  
Ezequiel Santillan ◽  
Hari Seshan ◽  
Florentin Constancias ◽  
Stefan Wuertz
Keyword(s):  
Life History ◽  
Network Analysis ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Microbial Communities ◽  
Bacterial Communities ◽  
Life History Strategies ◽  
System Response ◽  
Rrna Gene ◽  
Shotgun Metagenomics

SummaryTrait-based approaches are increasingly gaining importance in community ecology, as a way of finding general rules for the mechanisms driving changes in community structure and function under the influence of perturbations. Frameworks for life-history strategies have been successfully applied to describe changes in plant and animal communities upon disturbance. To evaluate their applicability to complex bacterial communities, we operated replicated wastewater treatment bioreactors for 35 days and subjected them to eight different disturbance frequencies of a toxic pollutant (3-chloroaniline), starting with a mixed inoculum from a full-scale treatment plant. Relevant ecosystem functions were tracked and microbial communities assessed through metagenomics and 16S rRNA gene sequencing. Combining a series of ordination, statistical and network analysis methods, we associated different life-history strategies with microbial communities across the disturbance range. These strategies were evaluated using tradeoffs in community function and genotypic potential, and changes in bacterial genus composition. We further compared our findings with other ecological studies and adopted a semi-quantitative CSR (competitors, ruderals, stress-tolerants) classification. The framework reduces complex datasets of microbial traits, functions, and taxa into ecologically meaningful components to help understand the system response to disturbance, and hence represents a promising tool for managing microbial communities.Originality-Significance StatementThis study establishes, for the first time, CSR life-history strategies in the context of bacterial communities. This framework is explained using community aggregated traits in an environment other than soil, also a first, using a combination of ordination methods, network analysis, and genotypic information from shotgun metagenomics and 16S rRNA gene amplicon sequencing.

PSIII-18 Identification of microbial interactions and markers associated with Shiga toxin-producing bacteria colonization in the rectum of beef steers

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 339-340
Author(s):  
Zhe Pan ◽  
Yanhong Chen ◽  
Tim A McAllister
Keyword(s):  
Network Analysis ◽  
Microbial Communities ◽  
Shiga Toxin ◽  
Amplicon Sequencing ◽  
Microbial Interactions ◽  
Beef Steers ◽  
Ecological Role ◽  
Dna And Rna ◽  
Microbial Profiling ◽  
Shiga Toxin 2

Abstract This study aimed to identify whether microbial interactions in the rectum contribute to Shiga toxin producing bacteria colonization. In total, 12 rectal digesta samples based on the previously identified Shiga toxin 2 gene (stx2) abundance (DNA) and expression (RNA) in Shiga toxin-producing bacteria (Stx2- group: detectable DNA, n=6; Stx2+ group: detectable DNA and RNA, n = 6) were subjected to microbial profiling using amplicon sequencing. Firmicutes (72.7 ± 2.0 %) and Bacteroidetes (24.6 ± 1.9 %) are the most predominant phyla of rectal microbiota, and no compositional differences were identified between two groups at the phylum level. The Shannon and Chao1 indices weren’t different in rectal digesta microbial communities between two groups. Twenty-four and thirteen taxa were identified to be group-specific genera in microbial communities from Stx2- and Stx2+ group, respectively (2 out of 6, average relative abundance >0.1%). The network analysis indicated 12 and 14 keystone taxa (Generalists, densely connected with other taxa) in microbial communities between Stx2- and Stx2+ groups, respectively. Eight out of 12 and six out of 14 generalists in the Stx2- and Stx2+ group are belonging to group-specific genera, respectively. Generalists belonging to group-specific genera were broadly distributed in Stx2- network while centralized distributed in the Stx2+ network, suggesting the higher stability of the Stx2- network structure in comparison of Stx2+ network computed by the natural connectivity measurement. However, 66 core genera shared by microbial communities between two groups were not classified into network generalists. Overall, our results indicate microbial crosstalks and keystone taxa in microbial communities between two groups differed, suggesting that the microbial interactions rather than the shifts in taxa abundance may be more important affecting host. Moreover, group-specific genera play a vital ecological role in the microbial interactions, indicating the potential for being microbial markers to differentiate Shiga toxin-producing bacteria colonization in beef cattle.

scholarly journals An exploratory study on the microbiome of northern and southern populations of Ixodes scapularis ticks predicts changes and unique bacterial interactions

2021 ◽  
Author(s):  
Deepak Kumar ◽  
Latoyia P. Downs ◽  
Abdulsalam Adegoke ◽  
Erika Machtinger ◽  
Kelly Oggenfuss ◽  
...  
Keyword(s):  
Network Analysis ◽  
Borrelia Burgdorferi ◽  
Microbial Communities ◽  
Ixodes Scapularis ◽  
The United States ◽  
Model Potential ◽  
Microbial Interactions ◽  
Borrelia Burgdorferi Sensu Lato ◽  
Metagenomic Sequencing ◽  
Significant Difference

The black-legged tick (Ixodes scapularis) is the primary vector of Borrelia burgdorferi, the causative agent of Lyme disease in North America. However, the prevalence of Lyme borreliosis is clustered around the northern states of the United States of America. This study utilized a metagenomic sequencing approach to compare the microbial communities residing within Ix. scapularis populations from north and southern geographic locations in the USA. Using a SparCC network construction model, potential interactions between members of the microbial communities from Borrelia burgdorferi-infected tissues of unfed and blood-fed ticks were performed. A significant difference in bacterial composition and diversity among northern and southern tick populations was found between northern and southern tick populations. The network analysis predicted a potential antagonistic interaction between endosymbiont Rickettsia buchneri and Borrelia burgdorferi sensu lato. Network analysis, as expected, predicted significant positive and negative microbial interactions in ticks from these geographic regions, with the genus Rickettsia, Francisella, and Borreliella playing an essential role in the identified clusters. Interactions between Rickettsia buchneri and Borrelia burgdorferi sensu lato needs more validation and understanding. Understanding the interplay between the micro-biome and tick-borne pathogens within tick vectors may pave the way for new strategies to prevent tick-borne infections.

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