Investigating a holobiont: Microbiota perturbations and transkingdom networks

Greer, Renee L.; Dong, Xiaoxi; Morgun, Andrey; Shulzhenko, Natalia

doi:10.1080/19490976.2015.1128625

Cited by 43 publications

(33 citation statements)

References 80 publications

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“…Although the dissection of these transkingdom interactions is evolving [159], we are still in the early stages of this journey. Indeed, epidemiological and even some mechanistic studies are accumulating.…”

Section: Discussionmentioning

confidence: 99%

Interplay between viruses and bacterial microbiota in cancer development

Vyshenska

Lam

Shulzhenko

et al. 2017

Seminars in Immunology

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During the last few decades we have become accustomed to the idea that viruses can cause tumors. It is much less considered and discussed, however, that most people infected with oncoviruses will never develop cancer. Therefore, the genetic and environmental factors that tip the scales from clearance of viral infection to development of cancer are currently an area of active investigation. Microbiota has recently emerged as a potentially critical factor that would affect this balance by increasing or decreasing the ability of viral infection to promote carcinogenesis. In this review, we provide a model of microbiome contribution to the development of oncogenic viral infections and viral associated cancers, give examples of this process in human tumors, and describe the challenges that prevent progress in the field as well as their potential solutions.

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Section: Discussionmentioning

confidence: 99%

Interplay between viruses and bacterial microbiota in cancer development

Vyshenska

Lam

Shulzhenko

et al. 2017

Seminars in Immunology

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“…Rather than existing as independent organisms, multi-cellular hosts together with their inhabiting microbial cells have been viewed as “metaorganisms” (also termed superorganisms or holobionts) [1]. Millions of commensals, symbiotic, and pathogenic microorganisms colonize our body.…”

Section: Introductionmentioning

confidence: 99%

“…To tackle the pathogenicity challenge, it is reasonable to concomitantly consider all host pathways and HMIs. The transkingdom (metaorganism) network analysis is a robust research framework that considers host and microbiota as a whole [1]. Systems biology approaches that integrate the HMIs with host endogenous protein interaction networks reveal the systematic trends in virulence strategies of pathogens.…”

Section: Introductionmentioning

confidence: 99%

“…The availability of genome-wide high throughput omics data makes it possible to associate microbiota with certain host phenotypes at multiple levels and construct host-pathogen interaction networks at the transcriptome [21], proteome [22], and metabolome levels [23]. Steps toward the construction of host-microbiota networks of gene [1], mRNA [24], protein-protein interaction (PPI) [25–28], and metabolic networks [29] have already been taken. Within this framework we highlight molecular mimicry, a common strategy that microorganisms exploit to bind to host proteins and perturb its physiological signaling.…”

Section: Introductionmentioning

confidence: 99%

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Structural host-microbiota interaction networks

2017

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Hundreds of different species colonize multicellular organisms making them “metaorganisms”. A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole–may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.

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“…Indeed, medical fields such as cardiology [8], endocrinology [10, 11], immunology [4, 12, 13], host-microbiome interactions [10, 12] and others have benefited from gene network analysis. Cancer is a very good example of applications of gene expression networks because insights from network analyses were essential for identification of key drivers of carcinogenesis for brain [14, 15], breast [15], skin [9] and cervical [16] tumors.…”

Section: Introductionmentioning

confidence: 99%

Unexpected links reflect the noise in networks

et al. 2016

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BackgroundGene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analysis.ResultsWe propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches. This method is based on a special relationship between sign of correlation (positive/negative) and directionality (up/down) of gene regulation, and allows for the identification and removal of approximately half of all erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we establish a mathematical foundation for our method. Analyzing existing biological datasets, we find a strong correlation between the results of our method and false discovery rate (FDR). Furthermore, simulation analysis demonstrates that our method provides a more accurate estimate of network error than FDR.ConclusionsThus, our study provides a new robust approach for improving reconstruction of covariation networks.ReviewersThis article was reviewed by Eugene Koonin, Sergei Maslov, Daniel Yasumasa Takahashi.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0155-0) contains supplementary material, which is available to authorized users.

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Investigating a holobiont: Microbiota perturbations and transkingdom networks

Cited by 43 publications

References 80 publications

Interplay between viruses and bacterial microbiota in cancer development

Interplay between viruses and bacterial microbiota in cancer development

Structural host-microbiota interaction networks

Unexpected links reflect the noise in networks

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