Network-Based Approaches Reveal Potential Therapeutic Targets for Host-Directed Antileishmanial Therapy Driving Drug Repurposing

Martínez-Hernández, J. Eduardo; Hammoud, Zaynab; Sousa, Alessandra Mara de; Monte-Neto, Rubens L.; Maracaja-Coutinho, Vinícius; Martin, Alberto J. M.

doi:10.1128/spectrum.01018-21

Cited by 10 publications

(6 citation statements)

References 98 publications

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“…GRNs often play a crucial role in identifying critical regulators of different types and the relationships between them, ranging from transcription factor (TF)-encoding gene and non-coding RNA (ncRNA) regulatory interactions in various cellular processes and signaling [38,39]. Transcriptomic expression data, molecular interaction networks used to build GRNs, and analysis methodologies that infer context-specific interaction networks have made it possible to compare physiological and pathological contexts to find potential early disease markers and master regulatory candidate genes in various pathological conditions, including periodontitis [29,[40][41][42][43][44]. However, there are no reports of massive sequencing to analyze transcriptomic data and create GRNs explaining how transcription is regulated during experimental periodontitis.…”

Section: Introductionmentioning

confidence: 99%

Transcriptional Signatures and Network-Based Approaches Identified Master Regulators Transcription Factors Involved in Experimental Periodontitis Pathogenesis

Vicencio,

Nuñez-Belmar,

Cardenas

et al. 2023

IJMS

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Periodontitis is a chronic inflammatory disease characterized by the progressive and irreversible destruction of the periodontium. Its aetiopathogenesis lies in the constant challenge of the dysbiotic biofilm, which triggers a deregulated immune response responsible for the disease phenotype. Although the molecular mechanisms underlying periodontitis have been extensively studied, the regulatory mechanisms at the transcriptional level remain unclear. To generate transcriptomic data, we performed RNA shotgun sequencing of the oral mucosa of periodontitis-affected mice. Since genes are not expressed in isolation during pathological processes, we disclose here the complete repertoire of differentially expressed genes (DEG) and co-expressed modules to build Gene Regulatory Networks (GRNs) and identify the Master Transcriptional Regulators of periodontitis. The transcriptional changes revealed 366 protein-coding genes and 42 non-coding genes differentially expressed and enriched in the immune response. Furthermore, we found 13 co-expression modules with different representation degrees and gene expression levels. Our GRN comprises genes from 12 gene clusters, 166 nodes, of which 33 encode Transcription Factors, and 201 connections. Finally, using these strategies, 26 master regulators of periodontitis were identified. In conclusion, combining the transcriptomic analyses with the regulatory network construction represents a powerful and efficient strategy for identifying potential periodontitis-therapeutic targets.

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Section: Introductionmentioning

confidence: 99%

Transcriptional Signatures and Network-Based Approaches Identified Master Regulators Transcription Factors Involved in Experimental Periodontitis Pathogenesis

Vicencio,

Nuñez-Belmar,

Cardenas

et al. 2023

IJMS

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“…while the edges can be the interaction between them. Network edges can be directed [93] , [94] , [95] , undirected [96] or weighted [97] , [98] . Mostly the quantitative information derived from high-throughput screening is used to construct the weighted networks where edge is represented by some numerical values.…”

Section: Computational Approaches To Drug Repurposingmentioning

confidence: 99%

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19

Ahmed

Soomro

Salih

et al. 2022

Biomedicine & Pharmacotherapy

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“…We selected Leishmania braziliensis MHOM/BR/1975/M2903, Leishmania donovani BPK282A1 (MHOM/NP/2002/BPK282A1) and Leishmania major Friedlin (MHOM/IL/1981/Friedlin) for subsequent analyzes, according to previously described parameters (47). Next, we employed an automated in-house pipeline for RNA-seq mapping and read counts measuring (120), with the following modifications. FastQC v0.11.8 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and Fastp (121) were used to evaluate and filter out low quality reads, considering a Phred cut-off value of Q = 30.…”

Section: Transcriptional Evidence Of Predicted Ncrnasmentioning

confidence: 99%

Comparative and systems analyses of Leishmania spp. non-coding RNAs through developmental stages

Martínez-Hernández

Aliaga-Tobar

González

et al. 2021

Preprint

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Leishmania spp. is the causal agent of several diseases called leishmaniases, neglected diseases that seek to be eradicated in the coming years. We aimed to study the genomic structure and function of non-coding RNAs (ncRNAs) from Leishmania spp. and to get insights into its RNAome. We studied 26 strains corresponding to 16 different species of Leishmania genus. RNAome analysis revealed the presence of several ncRNAs that are shared through different species, allowing us to differentiate between subgenus and as well as species that are canonically related to visceral leishmaniasis. We found coexpression relationships within coding genes and ncRNAs, thus suggesting possible functional relationships between different coding genes-ncRNAs. Expression analysis in the metacyclic developmental stage comparison for Leishmania braziliensis and Leishmania major reveals the presence of shared coexpressed ncRNAs to several other coding genes in both species involved in chromatin structure and host interaction. This work constitutes the first effort to characterize the Leishmania RNAome, supporting further approaches to better understand the role of ncRNAs in the gene regulation, infective process and host-parasite interaction.

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Network-Based Approaches Reveal Potential Therapeutic Targets for Host-Directed Antileishmanial Therapy Driving Drug Repurposing

Abstract: This work opens a new path to fight parasites by targeting host molecular functions by repurposing available and approved drugs. We created a novel approach to identify key proteins involved in any biological process by combining gene regulatory networks and expression profiles.

Cited by 10 publications

References 98 publications

Transcriptional Signatures and Network-Based Approaches Identified Master Regulators Transcription Factors Involved in Experimental Periodontitis Pathogenesis

Transcriptional Signatures and Network-Based Approaches Identified Master Regulators Transcription Factors Involved in Experimental Periodontitis Pathogenesis

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19

Comparative and systems analyses of Leishmania spp. non-coding RNAs through developmental stages

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