Perturbed human sub-networks by<i>Fusobacterium nucleatum</i>candidate virulence proteins

Zanzoni, Andreas; Spinelli, Lionel; Braham, Shérazade; Brun, Catherine

doi:10.1101/094136

Cited by 2 publications

(4 citation statements)

References 108 publications

(112 reference statements)

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“…27 F. nucleatum harbors several membrane-related and surface-associated proteins, whose function is to interact with microorganisms and host cells. 53,54 This kind of interaction may lead to the pathogenic ability of F. nucleatum to bind and/or invade multiple cell types including oral and colonic epithelial cells. 55,56…”

Section: Fusobacterium Nucleatummentioning

confidence: 99%

“…92 F. nucleatum harbors several membrane occupation and recognition nexus repeat-containing proteins, whose function is to interact with the microorganisms and the host cells. 53,54 Such an interaction acts as a groundwork for the pathogenic ability of F. nucleatum to bind and/or invade multiple cell types including oral and colonic epithelial cells. 55,56 The localization of F. nucleatum to cancerous tissues is mainly attributed to fusobacterial protein Fap2, which can recognize and bind to Gal/GalNAc, a membrane protein overexpressed in CRC cells.…”

Section: Colonization and Survival Of Oral Microbiota Inside Tissuesmentioning

confidence: 99%

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Role of the oral microbiota in cancer evolution and progression

Sun

Tang

et al. 2020

Cancer Medicine

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Bacteria identified in the oral cavity are highly complicated. They include approximately 1000 species with a diverse variety of commensal microbes that play crucial roles in the health status of individuals. Epidemiological studies related to molecular pathology have revealed that there is a close relationship between oral microbiota and tumor occurrence. Oral microbiota has attracted considerable attention for its role in in‐situ or distant tumor progression. Anaerobic oral bacteria with potential pathogenic abilities, especially Fusobacterium nucleatum and Porphyromonas gingivalis, are well studied and have close relationships with various types of carcinomas. Some aerobic bacteria such as Parvimonas are also linked to tumorigenesis. Moreover, human papillomavirus, oral fungi, and parasites are closely associated with oropharyngeal carcinoma. Microbial dysbiosis, colonization, and translocation of oral microbiota are necessary for implementation of carcinogenic functions. Various underlying mechanisms of oral microbiota‐induced carcinogenesis have been reported including excessive inflammatory reaction, immunosuppression of host, promotion of malignant transformation, antiapoptotic activity, and secretion of carcinogens. In this review, we have systemically described the impact of oral microbial abnormalities on carcinogenesis and the future directions in this field for bringing in new ideas for effective prevention of tumors.

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Section: Fusobacterium Nucleatummentioning

confidence: 99%

Section: Colonization and Survival Of Oral Microbiota Inside Tissuesmentioning

confidence: 99%

Role of the oral microbiota in cancer evolution and progression

Sun

Tang

et al. 2020

Cancer Medicine

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“…Over the last years, many computational methods have been developed to predict pathogen-host protein interactions, some of which are based on the detection of sequence or structural mimicry elements (Arnold et al ., 2012; Nourani et al ., 2015). Such approaches allowed, for instance, to suggest potential molecular mechanisms underlying the implication of gastrointestinal bacteria in human cancer (Zanzoni et al ., 2017; Guven-Maiorov et al ., 2017) or to discriminate between viral strains with different oncogenic potential (Lasso et al ., 2019), thus showing that protein-protein interaction predictions can be instrumental in untangling microbe-host disease associations. Nevertheless, the source code of many of these tools are not freely available to the community (e.g., (Becerra et al ., 2017; Guven-Maiorov et al ., 2017; Lasso et al ., 2019)) providing the predictions through a database (e.g., (Lasso et al ., 2019)), or can be only used through a web interface (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…In this context, and inspired by our previous work (Zanzoni et al ., 2017), we present mimic INT, a computational workflow for large-scale interaction inference between microbe and human proteins by detecting host-like elements and using experimentally identified interaction templates (Mosca et al ., 2014; Kumar et al ., 2020).…”

Section: Introductionmentioning

confidence: 99%

mimicINT: a workflow for microbe-host protein interaction inference

Choteau

Cristianini

Maldonado

et al. 2022

Preprint

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The increasing incidence of emerging infectious diseases is posing serious global threats. Therefore, there is a clear need for developing computational methods that can assist and speed-up experimental research to better characterize the molecular mechanisms of microbial infections. In this context, we developed mimicINT, a freely available computational workflow for large-scale protein-protein interaction inference between microbe and human by detecting putative molecular mimicry elements that can mediate the interaction with host proteins: short linear motifs (SLiMs) and host-like globular domains. mimicINT exploits these putative elements to infer the interaction with human proteins by using known templates of domain-domain and SLiM-domain interaction templates. mimicINT provides (i) robust Monte-Carlo simulations to assess the statistical significance of SLiM detection which suffers from false positive, and (ii) interaction specificity filter to account for differences between motif-binding domains of the same family. mimicINT is implemented in Python and R, and it is available at: https://github.com/TAGC-NetworkBiology/mimicINT.

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Perturbed human sub-networks byFusobacterium nucleatumcandidate virulence proteins

Cited by 2 publications

References 108 publications

Role of the oral microbiota in cancer evolution and progression

Role of the oral microbiota in cancer evolution and progression

mimicINT: a workflow for microbe-host protein interaction inference

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