ReviewPLoS computational biology2017
Structural host-microbiota interaction networks.
Review in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 54 citations in OpenAlex.
- Differential analysis of microbial interaction networks.Briefings in bioinformatics · 2026Article
- When inflammatory stressors dramatically change, disease phenotypes may transform between autoimmune hematopoietic failure and myeloid neoplasms.Frontiers in immunology · 2024Review
- Tuberculosis-induced aplastic crisis and atypical lymphocyte expansion in advanced myelodysplastic syndrome: A case report and review of literature.World journal of clinical cases · 2023Article
- Bowel inflammatory presentations on computed tomography in adult patients with severe aplastic anemia during flared inflammatory episodes.World journal of clinical cases · 2023Article
- Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions.Frontiers in oncology · 2023Review
- HMI-PRED 2.0: a biologist-oriented web application for prediction of host-microbe protein-protein interaction by interface mimicry.Bioinformatics (Oxford, England) · 2022Article
- AlphaFold, Artificial Intelligence (AI), and Allostery.The journal of physical chemistry. B · 2022Review
- Article
- Host-microbiome protein-protein interactions capture disease-relevant pathways.Genome biology · 2022Article
- Challenges and opportunities in network-based solutions for biological questions.Briefings in bioinformatics · 2022Article
- Dynamic, but Not Necessarily Disordered, Human-Virus Interactions Mediated through SLiMs in Viral Proteins.Viruses · 2021Review
- ViralLink: An integrated workflow to investigate the effect of SARS-CoV-2 on intracellular signalling and regulatory pathways.PLoS computational biology · 2021Article
- Transkingdom network analysis provides insight into host-microbiome interactions in Atlantic salmon.Computational and structural biotechnology journal · 2021Article
- Article
- HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry.Journal of molecular biology · 2020Article
- Predicting host taxonomic information from viral genomes: A comparison of feature representations.PLoS computational biology · 2020Article
- An Integrated Multi-Disciplinary Perspectivefor Addressing Challenges of the Human Gut Microbiome.Metabolites · 2020Review
- Gut-host Crosstalk: Methodological and Computational Challenges.Digestive diseases and sciences · 2020Review
- Trends in Symbiont-Induced Host Cellular Differentiation.Results and problems in cell differentiation · 2020Article
- Potential Elimination of Human Gut Resistome by Exploiting the Benefits of Functional Foods.Frontiers in microbiology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 2 countries.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.