Two Decades of Biological Pathway Databases: Results and Challenges

Rahmati, Sara; Pastrello, Chiara; Rossos, Andrea E. M.; Jurišica, Igor

doi:10.1016/b978-0-12-809633-8.20496-2

Cited by 5 publications

(2 citation statements)

References 69 publications

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“…Differential mRNA (gene) expression (DEG) followed by pathway enrichment analysis is a common practice in cell biology and clinical research to study relationship between genetics and phenotypes 5 , 6 that has been used in psoriatic disease research too. For example DEGs among psoriasis patients initiating tumour necrosis factor inhibitors (TNFi) and interleukin-17A inhibitors (IL-17Ai) with both lesional and non-lesional skin have been investigated 7 , 8 .…”

Section: Introductionmentioning

confidence: 99%

Rho-GTPase pathways may differentiate treatment response to TNF-alpha and IL-17A inhibitors in psoriatic arthritis

Rahmati

O’Rielly

et al. 2020

Sci Rep

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Biological therapies have dramatically improved the therapeutic landscape of psoriatic arthritis (PsA); however, 40–50% of patients are primary non-responders with response rates declining significantly with each successive biological therapy. Therefore, there is a pressing need to develop a coherent strategy for effective initial and subsequent selection of biologic agents. We interrogated 40 PsA patients initiating either tumour necrosis factor inhibitors (TNFi) or interleukin-17A inhibitors (17Ai) for active PsA. Patients achieving low disease activity according to the Disease Activity Index for PsA (DAPSA) at 3 months were classified as responders. Baseline and 3-month CD4+ transcript profiling were performed, and novel signaling pathways were identified using a multi-omics profiling and integrative computational analysis approach. Using transcriptomic data at initiation of therapy, we identified over 100 differentially expressed genes (DEGs) that differentiated IL-17Ai response from non-response and TNFi response from non-response. Integration of cell-type-specific DEGs with protein–protein interactions and further comprehensive pathway enrichment analysis revealed several pathways. Rho GTPase signaling pathway exhibited a strong signal specific to IL-17Ai response and the genes, RAC1 and ROCKs, are supported by results from prior research. Our detailed network and pathway analyses have identified the rewiring of Rho GTPase pathways as potential markers of response to IL17Ai but not TNFi. These results need further verification.

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Section: Introductionmentioning

confidence: 99%

Rho-GTPase pathways may differentiate treatment response to TNF-alpha and IL-17A inhibitors in psoriatic arthritis

Rahmati

O’Rielly

et al. 2020

Sci Rep

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“…Generic pathway databases (including pathways from all three categories) are larger and more frequently used. Several generic pathway databases are available, some stemming from large curation efforts (primary databases, the oldest being KEGG [ 11 ] and the largest being Reactome [ 12 ]), some integrating data from primary databases (integrated databases, the oldest being PantherDB [ 13 ] and the largest being PathwayCommons [ 14 ]) and some using a hybrid integration-curation approach (hybrid databases, the oldest and largest being WikiPathways [ 15 , 16 ]). Nonetheless, each database includes a largely different number of pathways, annotates only part of the genome and has poor overlap with other databases.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive pathway enrichment analysis workflows: COVID-19 case study

Agapito

Pastrello

Jurišica

2020

Briefings in Bioinformatics

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The coronavirus disease 2019 (COVID-19) outbreak due to the novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been classified as a pandemic disease by the World Health Organization on the 12th March 2020. This world-wide crisis created an urgent need to identify effective countermeasures against SARS-CoV-2. In silico methods, artificial intelligence and bioinformatics analysis pipelines provide effective and useful infrastructure for comprehensive interrogation and interpretation of available data, helping to find biomarkers, explainable models and eventually cures. One class of such tools, pathway enrichment analysis (PEA) methods, helps researchers to find possible key targets present in biological pathways of host cells that are targeted by SARS-CoV-2. Since many software tools are available, it is not easy for non-computational users to choose the best one for their needs. In this paper, we highlight how to choose the most suitable PEA method based on the type of COVID-19 data to analyze. We aim to provide a comprehensive overview of PEA techniques and the tools that implement them.

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