PathWalks: Identifying pathway communities using a disease-related map of integrated information

Karatzas, Evangelos; Zachariou, Margarita; Bourdakou, Marilena M.; Minadakis, George; Oulas, Anastasis; Kolios, George; Delis, Alex

doi:10.1101/2020.01.27.921270

Cited by 5 publications

(12 citation statements)

References 66 publications

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“…The results obtained from PathWalks [ 18 ] for each HD stage include (i) a list of ranked pathways (which are the most visited by the random walker) and (ii) a list of ranked edges (the most frequently traversed edges). The score indicates the times a respective pathway or edge was accessed by the random walker.…”

Section: Resultsmentioning

confidence: 99%

“…Through the utilization of multiple bioinformatics tools such as PathwayConnector [ 15 ], GeneTrail3 [ 17 ] and PathWalks [ 18 ] we were able to highlight both pathways and communities of pathways (based on ranked edges in the pathway-pathway network) in both pre-symptomatic and symptomatic HD patients.…”

Section: Discussionmentioning

confidence: 99%

“…The up-regulated TGF-β1 has been associated with CNS injury and neurodegeneration [ 17 ]. Dysregulation of TGF-β signaling is possibly involved in the HD pathology [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

“…PathWalks is an approach, where a random walker, walks across a pathway-to-pathway network with the help a gene network which was constructed by integrating multi-source information regarding a disease of interest [ 18 ]. The two main networks required by PathWalks is (i) a multi-source integration, which is the synthetic gene network that represents integrated information such as gene co-expression, miRNA and physical interactions obtained from biological database and (ii) pathway-to-pathway network.…”

Section: Methodsmentioning

confidence: 99%

“…The two main networks required by PathWalks is (i) a multi-source integration, which is the synthetic gene network that represents integrated information such as gene co-expression, miRNA and physical interactions obtained from biological database and (ii) pathway-to-pathway network. The walker performs random walks on the gene-to-gene network and the nodes visited by the walker indicates the walker’s destination on the pathway-to-pathway network [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

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Investigating the Transition of Pre-Symptomatic to Symptomatic Huntington’s Disease Status Based on Omics Data

Christodoulou

Zachariou

Tomazou

et al. 2020

IJMS

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Huntington’s disease is a rare neurodegenerative disease caused by a cytosine–adenine–guanine (CAG) trinucleotide expansion in the Huntingtin (HTT) gene. Although Huntington’s disease (HD) is well studied, the pathophysiological mechanisms, genes and metabolites involved in HD remain poorly understood. Systems bioinformatics can reveal synergistic relationships among different omics levels and enables the integration of biological data. It allows for the overall understanding of biological mechanisms, pathways, genes and metabolites involved in HD. The purpose of this study was to identify the differentially expressed genes (DEGs), pathways and metabolites as well as observe how these biological terms differ between the pre-symptomatic and symptomatic HD stages. A publicly available dataset from the Gene Expression Omnibus (GEO) was analyzed to obtain the DEGs for each HD stage, and gene co-expression networks were obtained for each HD stage. Network rewiring, highlights the nodes that change most their connectivity with their neighbors and infers their possible implication in the transition between different states. The CACNA1I gene was the mostly highly rewired node among pre-symptomatic and symptomatic HD network. Furthermore, we identified AF198444 to be common between the rewired genes and DEGs of symptomatic HD. CNTN6, DEK, LTN1, MST4, ZFYVE16, CEP135, DCAKD, MAP4K3, NUPL1 and RBM15 between the DEGs of pre-symptomatic and DEGs of symptomatic HD and CACNA1I, DNAJB14, EPS8L3, HSDL2, SNRPD3, SOX12, ACLY, ATF2, BAG5, ERBB4, FOCAD, GRAMD1C, LIN7C, MIR22, MTHFR, NABP1, NRG2, OTC, PRAMEF12, SLC30A10, STAG2 and Y16709 between the rewired genes and DEGs of pre-symptomatic HD. The proteins encoded by these genes are involved in various biological pathways such as phosphatidylinositol-4,5-bisphosphate 3-kinase activity, cAMP response element-binding protein binding, protein tyrosine kinase activity, voltage-gated calcium channel activity, ubiquitin protein ligase activity, adenosine triphosphate (ATP) binding, and protein serine/threonine kinase. Additionally, prominent molecular pathways for each HD stage were then obtained, and metabolites related to each pathway for both disease stages were identified. The transforming growth factor beta (TGF-β) signaling (pre-symptomatic and symptomatic stages of the disease), calcium (Ca2+) signaling (pre-symptomatic), dopaminergic synapse pathway (symptomatic HD patients) and Hippo signaling (pre-symptomatic) pathways were identified. The in silico metabolites we identified include Ca2+, inositol 1,4,5-trisphosphate, sphingosine 1-phosphate, dopamine, homovanillate and L-tyrosine. The genes, pathways and metabolites identified for each HD stage can provide a better understanding of the mechanisms that become altered in each disease stage. Our results can guide the development of therapies that may target the altered genes and metabolites of the perturbed pathways, leading to an improvement in clinical symptoms and hopefully a delay in the age of onset.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…The up-regulated TGF-β1 has been associated with CNS injury and neurodegeneration [ 17 ]. Dysregulation of TGF-β signaling is possibly involved in the HD pathology [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Investigating the Transition of Pre-Symptomatic to Symptomatic Huntington’s Disease Status Based on Omics Data

Christodoulou

Zachariou

Tomazou

et al. 2020

IJMS

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PathIN: an integrated tool for the visualization of pathway interaction networks

Minadakis

Christodoulou

Tsouloupas

et al. 2023

Computational and Structural Biotechnology Journal

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Drug Repurposing Through a Bioinformatics Pipeline Applied on Fibrotic Diseases

Karatzas¹,

Kakouri

Kolios

et al. 2020

Preprint

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AbstractSubjectFibrotic diseases cover a spectrum of systemic and organ-specific maladies that affect a large portion of the population, currently without cure. The shared characteristic these diseases feature is their uncontrollable fibrogenesis deemed responsible for the accumulated damage in the susceptible tissues. Idiopathic Pulmonary Fibrosis (IPF), an interstitial lung disease, is one of the most common and studied fibrotic diseases and still remains an active research target.ObjectiveWe highlight unique and common (i) genes, (ii) biological pathways and (iii) candidate repurposed drugs among nine fibrotic diseases. We bibliographically explore the resulting candidate substances for potential anti-fibrotic mode of action and focus on diseases that appear to be more similar to IPF so as to jointly examine potential treatments.MethodologyWe identify key genes for the 9 fibrotic diseases by analyzing transcriptomics datasets. We construct gene-to-gene networks for each disease and examine these networks to explore functional communities of biological pathways. We also use the most significant genes as input in Drug Repurposing (DR) tools and re-rank the resulting candidates according to their structural properties and functional relationship to each investigated disease.ResultsWe identify 7 biological pathways involved in all 9 fibrotic diseases as well as pathways unique to some of these diseases. Based on our DR results, we suggest captopril and ibuprofen that both appear to slow the progression of fibrotic diseases according to existing bibliography. We also recommend nafcillin and memantine, which haven’t been studied against fibrosis yet, for further wet-lab experimentation. We also observe a group of cardiomyopathy-related pathways that are exclusively highlighted for Oral Submucous Fibrosis (OSF). We suggest digoxin to be tested against OSF, since we observe cardiomyopathy-related pathways implicated in OSF and there is bibliographic evidence that digoxin may potentially clear myocardial fibrosis. Finally, we establish that IPF shares several involved genes, biological pathways and candidate inhibiting-drugs with Dupuytren’s Disease, IgG4-related Disease, SSc and Cystic Fibrosis. We propose that treatments for these fibrotic diseases should be jointly pursued.

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PathWalks: Identifying pathway communities using a disease-related map of integrated information

Cited by 5 publications

References 66 publications

Investigating the Transition of Pre-Symptomatic to Symptomatic Huntington’s Disease Status Based on Omics Data

Investigating the Transition of Pre-Symptomatic to Symptomatic Huntington’s Disease Status Based on Omics Data

PathIN: an integrated tool for the visualization of pathway interaction networks

Drug Repurposing Through a Bioinformatics Pipeline Applied on Fibrotic Diseases

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