scholarly journals Temporal patterns in Ixodes ricinus microbial communities: an insight into tick-borne microbe interactions

2020 ◽  
Author(s):  
E Lejal ◽  
J Chiquet ◽  
J Aubert ◽  
S Robin ◽  
A Estrada-Peña ◽  
...  
Keyword(s):  
Network Analysis ◽  
Microbial Communities ◽  
Ixodes Ricinus ◽  
Urban Forest ◽  
Control Strategies ◽  
Parasitoid Wasp ◽  
Microbial Interactions ◽  
Data Set ◽  
New Findings ◽  
Strong Relationships

AbstractBackgroundTicks transmit pathogens of medical and veterinary importance, and represent an increasing threat for human and animal health. Important steps in assessing disease risk and developing possible new future control strategies involve identifying tick-borne microbes, their temporal dynamics and interactions.MethodsUsing high throughput sequencing, we studied the microbiota dynamics of Ixodes ricinus from 371 nymphs collected monthly over three consecutive years in a peri-urban forest. After adjusting a Poisson Log Normal model to our data set, the implementation of a principal component analysis as well as sparse network reconstruction and differential analysis allowed us to assess inter-annual, seasonal and monthly variability of I. ricinus microbial communities as well as their interactions.ResultsAround 75% of the detected sequences belonged to five genera known to be maternally inherited bacteria in arthropods and potentially circulating in ticks: Candidatus Midichloria, Rickettsia, Spiroplasma, Arsenophonus and Wolbachia. The structure of the I. ricinus microbiota was temporally variable with interannual recurrence and seemed to be mainly driven by OTUs belonging to environmental genera. The total network analysis revealed a majority of positive (partial) correlations. We identified strong relationships between OTUs belonging to Wolbachia and Arsenophonus, betraying the presence of the parasitoid wasp Ixodiphagus hookeri in ticks, and the well known arthropod symbiont Spiroplasma, previously documented to be involved in the defense against parasitoid wasp in Drosophila melanogaster. Other associations were observed between the tick symbiont Candidatus Midichloria and pathogens belonging to Rickettsia, probably Rickettsia helvetica. More specific network analysis finally suggested that the presence of pathogens belonging to genera Borrelia, Anaplasma and Rickettsia might disrupt microbial interactions in I. ricinus.ConclusionsHere, we identified the I. ricinus microbiota and documented for the first time the existence and recurrence of marked temporal shifts in the tick microbial community dynamics. We statistically showed strong relationships between the presence of some pathogens and the structure of the I. ricinus non-pathogenic microbes. We interestingly detected close links between some tick symbionts and the potential presence of either pathogenic Rickettsia or a parasitoid in ticks. All these new findings might be very promising for the future development of new control strategies of ticks and tick-borne diseases.

scholarly journals Temporal patterns in Ixodes ricinus microbial communities: an insight into tick-borne microbe interactions

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
E. Lejal ◽  
J. Chiquet ◽  
J. Aubert ◽  
S. Robin ◽  
A. Estrada-Peña ◽  
...  
Keyword(s):  
Ixodes Ricinus ◽  
Urban Forest ◽  
Temporal Dynamics ◽  
Disease Risk ◽  
Microbial Interactions ◽  
Data Set ◽  
Network Analyses ◽  
New Findings ◽  
Over Time ◽  
Strong Relationships

Abstract Background Ticks transmit pathogens of medical and veterinary importance and are an increasing threat to human and animal health. Assessing disease risk and developing new control strategies requires identifying members of the tick-borne microbiota as well as their temporal dynamics and interactions. Methods Using high-throughput sequencing, we studied the Ixodes ricinus microbiota and its temporal dynamics. 371 nymphs were monthly collected during three consecutive years in a peri-urban forest. After a Poisson lognormal model was adjusted to our data set, a principal component analysis, sparse network reconstruction, and differential analysis allowed us to assess seasonal and monthly variability of I. ricinus microbiota and interactions within this community. Results Around 75% of the detected sequences belonged to five genera known to be maternally inherited bacteria in arthropods and to potentially circulate in ticks: Candidatus Midichloria, Rickettsia, Spiroplasma, Arsenophonus and Wolbachia. The structure of the I. ricinus microbiota varied over time with interannual recurrence and seemed to be mainly driven by OTUs commonly found in the environment. Total network analysis revealed a majority of positive partial correlations. We identified strong relationships between OTUs belonging to Wolbachia and Arsenophonus, evidence for the presence of the parasitoid wasp Ixodiphagus hookeri in ticks. Other associations were observed between the tick symbiont Candidatus Midichloria and pathogens belonging to Rickettsia. Finally, more specific network analyses were performed on TBP-infected samples and suggested that the presence of pathogens belonging to the genera Borrelia, Anaplasma and Rickettsia may disrupt microbial interactions in I. ricinus. Conclusions We identified the I. ricinus microbiota and documented marked shifts in tick microbiota dynamics over time. Statistically, we showed strong relationships between the presence of specific pathogens and the structure of the I. ricinus microbiota. We detected close links between some tick symbionts and the potential presence of either pathogenic Rickettsia or a parasitoid in ticks. These new findings pave the way for the development of new strategies for the control of ticks and tick-borne diseases.

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.

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.

A peek in the micro-sized world: a review of design principles, engineering tools, and applications of engineered microbial community

2020 ◽  
Vol 48 (2) ◽  
pp. 399-409
Author(s):  
Baizhen Gao ◽  
Rushant Sabnis ◽  
Tommaso Costantini ◽  
Robert Jinkerson ◽  
Qing Sun
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Environmental Remediation ◽  
Real Life ◽  
Design Principles ◽  
Microbial Interactions ◽  
Potential Applications ◽  
New Gene ◽  
Global Biogeochemical Cycles ◽  
Synthetic Microbial Communities

Microbial communities drive diverse processes that impact nearly everything on this planet, from global biogeochemical cycles to human health. Harnessing the power of these microorganisms could provide solutions to many of the challenges that face society. However, naturally occurring microbial communities are not optimized for anthropogenic use. An emerging area of research is focusing on engineering synthetic microbial communities to carry out predefined functions. Microbial community engineers are applying design principles like top-down and bottom-up approaches to create synthetic microbial communities having a myriad of real-life applications in health care, disease prevention, and environmental remediation. Multiple genetic engineering tools and delivery approaches can be used to ‘knock-in' new gene functions into microbial communities. A systematic study of the microbial interactions, community assembling principles, and engineering tools are necessary for us to understand the microbial community and to better utilize them. Continued analysis and effort are required to further the current and potential applications of synthetic microbial communities.

Exploring fake news identification using word and sentence embeddings

2021 ◽  
pp. 1-8
Author(s):  
V.T Priyanga ◽  
J.P Sanjanasri ◽  
Vijay Krishna Menon ◽  
E.A Gopalakrishnan ◽  
K.P Soman
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Supervised Machine Learning ◽  
Breeding Ground ◽  
Fake News ◽  
Data Set ◽  
Highly Correlated ◽  
Use Of Social Media ◽  
The Liar

The widespread use of social media like Facebook, Twitter, Whatsapp, etc. has changed the way News is created and published; accessing news has become easy and inexpensive. However, the scale of usage and inability to moderate the content has made social media, a breeding ground for the circulation of fake news. Fake news is deliberately created either to increase the readership or disrupt the order in the society for political and commercial benefits. It is of paramount importance to identify and filter out fake news especially in democratic societies. Most existing methods for detecting fake news involve traditional supervised machine learning which has been quite ineffective. In this paper, we are analyzing word embedding features that can tell apart fake news from true news. We use the LIAR and ISOT data set. We churn out highly correlated news data from the entire data set by using cosine similarity and other such metrices, in order to distinguish their domains based on central topics. We then employ auto-encoders to detect and differentiate between true and fake news while also exploring their separability through network analysis.

scholarly journals Bayesian Network Structure and Predictability of Autistic Traits

2020 ◽  
pp. 003329412097815
Author(s):  
Giovanni Briganti ◽  
Donald R. Williams ◽  
Joris Mulder ◽  
Paul Linkowski
Keyword(s):  
Sex Differences ◽  
Network Analysis ◽  
Bayesian Network ◽  
Network Structure ◽  
Network Connectivity ◽  
Autistic Traits ◽  
Data Set ◽  
Conditional Dependence ◽  
Bayesian Network Structure ◽  
Male Subjects

The aim of this work is to explore the construct of autistic traits through the lens of network analysis with recently introduced Bayesian methods. A conditional dependence network structure was estimated from a data set composed of 649 university students that completed an autistic traits questionnaire. The connectedness of the network is also explored, as well as sex differences among female and male subjects in regard to network connectivity. The strongest connections in the network are found between items that measure similar autistic traits. Traits related to social skills are the most interconnected items in the network. Sex differences are found between female and male subjects. The Bayesian network analysis offers new insight on the connectivity of autistic traits as well as confirms several findings in the autism literature.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis

2007 ◽  
Vol 56 (6) ◽  
pp. 75-83 ◽  
Author(s):  
X. Flores ◽  
J. Comas ◽  
I.R. Roda ◽  
L. Jiménez ◽  
K.V. Gernaey
Keyword(s):  
Cluster Analysis ◽  
Control Strategies ◽  
Treatment Plant ◽  
Principal Component ◽  
Statistical Techniques ◽  
Data Sets ◽  
Data Set ◽  
Casual Relation ◽  
Evaluation Matrix ◽  
Natural Groups

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

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.

Sign in / Sign up

Export Citation Format

Share Document