Identification of metabolic pathways using pathfinding approaches: a systematic review

Algfoor, Zeyad Abd; Sunar, Mohd Shahrizal; Abdullah, Afnizanfaizal; Kolivand, Hoshang

doi:10.1093/bfgp/elw002

Cited by 16 publications

(12 citation statements)

References 105 publications

(120 reference statements)

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“…Since there was no statistical difference in the age and gender of pre‐FIT and FIT triage patients, we felt we were justified in comparing the two cohorts. The CRC detection rate of 3.2% in the FIT triage and 2.8% in the pre‐FIT group were similar suggesting that at the very least FIT was a safe triage method with rates similar to the 3.1% in the year 2019–2020 [ 11 ]. Pre‐COVID‐19, studies have demonstrated the potential value of FIT in CRC detection and triage; and since one study was from a local trust the local cancer alliance had even more confidence in the FIT as a triage tool [ 12 , 13 , 14 ].…”

Section: Discussionmentioning

confidence: 99%

The use of the faecal immunochemical test during the COVID‐19 pandemic to triage urgent colorectal cancer referrals

et al. 2022

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Aim During the first wave of the COVID‐19 pandemic in 2020, elective gastrointestinal endoscopy services were abbreviated for fear of viral transmission. However, urgent suspected colorectal cancer (CRC) referrals continued. Serendipitously, a national study suggested that a new faecal immunochemical test (FIT) might be helpful in triaging patients with colorectal alarm symptoms. Methods This was a single centre observational study of patients referred using NG12 criteria between March and August 2020. Patients were triaged to the urgent cancer pathway for FIT ≥ 10 μg/g and investigated using the latest National Health Service England guidance. Demographic data, method of investigations, cancer and polyp detection rates were compared to patients referred in the 6 months prior to the use of FIT as a triage tool. Results In all, 1192 patients (median age 70) were referred using NG12 guidelines during the pandemic period, compared with 1592 patients (median age 72) in the previous 6 months. CRC detection was similar in both groups ( n = 45, 2.8% vs. n = 38, 3.5%; P = 0.248). Two patients with a negative FIT (0.36%) had CRC. Using FIT as a triage tool resulted in a significant reduction in the use of endoscopy ( n = 477, 43.6% vs. n = 1186, 74.5%; P > 0.001) with a significant increase in CT scanning ( n = 696, 63.6% vs. n = 750, 47.1%; P < 0.001). Conclusion The use of FIT in NG12 patients triaged during the first wave of the COVID‐19 pandemic reduced endoscopy but not CT scanning and did not compromise CRC detection rates. It is a safe method that aids in reducing the burden on services greatly. A negative FIT test does not absolutely exclude CRC.

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Section: Discussionmentioning

confidence: 99%

The use of the faecal immunochemical test during the COVID‐19 pandemic to triage urgent colorectal cancer referrals

et al. 2022

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“…KEGG Mapper tools ( Kanehisa and Sato, 2020 ) also allow automatic assignment of enzymatic roles and pathway contextualization using genomes or proteomes as input and relies on the KEGG ontology to perform annotations based on sequence similarity. Other tools, reviewed elsewhere, allow additional curation and pathway-specific analysis to be performed once a draft reconstruction is attained ( Pitkänen et al, 2010 ; Abd Algfoor et al, 2017 ). Common to all described tools is their dependency upon a vocabulary of metabolic elements, or ontology, which depicts the complex, and often multi-level relationships among genes, proteins, enzymes, biochemical reactions, and regulators ( Stobbe et al, 2012 ).…”

Section: How To Prioritize Drug Targets In Pathogenic Bacteria?mentioning

confidence: 99%

From Genome to Drugs: New Approaches in Antimicrobial Discovery

et al. 2021

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Decades of successful use of antibiotics is currently challenged by the emergence of increasingly resistant bacterial strains. Novel drugs are urgently required but, in a scenario where private investment in the development of new antimicrobials is declining, efforts to combat drug-resistant infections become a worldwide public health problem. Reasons behind unsuccessful new antimicrobial development projects range from inadequate selection of the molecular targets to a lack of innovation. In this context, increasingly available omics data for multiple pathogens has created new drug discovery and development opportunities to fight infectious diseases. Identification of an appropriate molecular target is currently accepted as a critical step of the drug discovery process. Here, we review how diverse layers of multi-omics data in conjunction with structural/functional analysis and systems biology can be used to prioritize the best candidate proteins. Once the target is selected, virtual screening can be used as a robust methodology to explore molecular scaffolds that could act as inhibitors, guiding the development of new drug lead compounds. This review focuses on how the advent of omics and the development and application of bioinformatics strategies conduct a “big-data era” that improves target selection and lead compound identification in a cost-effective and shortened timeline.

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“…While metabolism has traditionally been divided into metabolic pathways, subsystems of metabolism dealing with specific functions, it has become increasingly clear that metabolism operates as a highly integrated network [1,2]. The research on the metabolomics field has considerably increased since the early 2000s, from the analysis of single pathways [3][4][5][6], to the comparative analysis of a set of pathways [7], together with the metabolic networks dynamics [8,9]. All these studies are aimed to find out how the metabolism of different species has evolved in order to discern their associated metabolic functions (see for example [10,11]), which are important for studying diseases and identifying pharmacological targets (as explored in [12,13]).…”

Section: Introductionmentioning

confidence: 99%

MetNet: A two-level approach to reconstructing and comparing metabolic networks

et al. 2021

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Metabolic pathway comparison and interaction between different species can detect important information for drug engineering and medical science. In the literature, proposals for reconstructing and comparing metabolic networks present two main problems: network reconstruction requires usually human intervention to integrate information from different sources and, in metabolic comparison, the size of the networks leads to a challenging computational problem. We propose to automatically reconstruct a metabolic network on the basis of KEGG database information. Our proposal relies on a two-level representation of the huge metabolic network: the first level is graph-based and depicts pathways as nodes and relations between pathways as edges; the second level represents each metabolic pathway in terms of its reactions content. The two-level representation complies with the KEGG database, which decomposes the metabolism of all the different organisms into “reference” pathways in a standardised way. On the basis of this two-level representation, we introduce some similarity measures for both levels. They allow for both a local comparison, pathway by pathway, and a global comparison of the entire metabolism. We developed a tool, MetNet, that implements the proposed methodology. MetNet makes it possible to automatically reconstruct the metabolic network of two organisms selected in KEGG and to compare their two networks both quantitatively and visually. We validate our methodology by presenting some experiments performed with MetNet.

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Identification of metabolic pathways using pathfinding approaches: a systematic review

Cited by 16 publications

References 105 publications

The use of the faecal immunochemical test during the COVID‐19 pandemic to triage urgent colorectal cancer referrals

The use of the faecal immunochemical test during the COVID‐19 pandemic to triage urgent colorectal cancer referrals

From Genome to Drugs: New Approaches in Antimicrobial Discovery

MetNet: A two-level approach to reconstructing and comparing metabolic networks

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