Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis

Fasano, Candida; Grossi, Valentina; Forte, Giovanna; Simone, Cristiano

doi:10.3390/cells11233739

Cited by 6 publications

(3 citation statements)

References 175 publications

(203 reference statements)

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“…Pathogens like SARS-CoV-2 mimic ELMs to gain entry into the cell ( Kruse et al 2021 , Mészáros et al 2021 ), and mutations in sequences containing ELMs contribute to diseases like cancer ( Uyar et al 2014 , Mészáros et al 2017 ). As a result, ELM-mediated protein interactions are potential targets for therapeutic intervention ( Mészáros et al 2021 , Fasano et al 2022 , Simonetti et al 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Fast and scalable querying of eukaryotic linear motifs with gget elm

Luebbert,

Hoang,

Kumar

et al. 2024

Bioinformatics

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Motivation Eukaryotic linear motifs (ELMs), or Short Linear Motifs (SLiMs), are protein interaction modules that play an essential role in cellular processes and signaling networks and are often involved in diseases like cancer. The ELM database is a collection of manually curated motif knowledge from scientific papers. It has become a crucial resource for investigating motif biology and recognizing candidate ELMs in novel amino acid sequences. Users can search amino acid sequences or UniProt Accessions on the ELM resource web interface. However, as with many web services, there are limitations in the swift processing of large-scale queries through the ELM web interface or API calls, and, therefore, integration into protein function analysis pipelines is limited. Results To allow swift, large-scale motif analyses on protein sequences using ELMs curated in the ELM database, we have extended the gget suite of Python and command line tools with a new module, gget elm, which does not rely on the ELM server for efficiently finding candidate ELMs in user-submitted amino acid sequences and UniProt Accessions. gget elm increases accessibility to the information stored in the ELM database and allows scalable searches for motif-mediated interaction sites in the amino acid sequences. Availability The manual and source code are available at https://github.com/pachterlab/gget. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

Fast and scalable querying of eukaryotic linear motifs with gget elm

Luebbert,

Hoang,

Kumar

et al. 2024

Bioinformatics

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show abstract

“…Pathogens like SARS-CoV2 mimic ELMs to gain entry into the cell (Kruse et al ., 2021; Mészáros et al ., 2021), and mutations in sequences containing ELMs contribute to diseases like cancer (Uyar et al ., 2014; Mészáros et al ., 2017). As a result, ELM-mediated protein interactions are potential targets for therapeutic intervention (Mészáros et al ., 2021; Simonetti et al ., 2023; Fasano et al ., 2022).…”

Section: Introductionmentioning

confidence: 99%

Fast and scalable querying of eukaryotic linear motifs withgget elm

Luebbert,

Hoang,

Kumar

et al. 2023

Preprint

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MotivationEukaryotic linear motifs (ELMs), or Short Linear Motifs (SLiMs), are protein interaction modules that play an essential role in cellular processes and signaling networks and are often involved in diseases like cancer. The ELM database is a collection of manually curated motif knowledge from scientific papers. It has become a crucial resource for cataloging motif biology and recognizing candidate ELMs in novel amino acid sequences. Users can search amino acid sequences or UniProt IDs on the ELM resource web interface. However, as with many web services, there are limitations in the swift processing of large-scale queries through the ELM web interface or API calls, and, therefore, integration into protein function analysis pipelines is limited.ResultsTo allow swift, large-scale motif analyses on protein sequences using ELMs curated on the ELM database, we have developed a Python and command line tool,gget elm, which relies on local computations for efficiently finding candidate ELMs in user-submitted amino acid sequences and UniProt identifiers.gget elmincreases accessibility to the information stored in the ELM database and allows scalable searches for motif-mediated interaction sites in the amino acid sequences.Availability and implementationThe manual and source code are available athttps://github.com/pachterlab/gget.

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“…CRC begins in the inner layers of the colon, rectum, and appendix and arises due to microsatellite and chromosomal instability [ 2 ], the accumulation of various genetic mutations (base substitutions, binding site mutations, and deletions), epigenetic modifications (e.g., non-coding RNA silencing, aberrant DNA methylation, histone acetylation modifications, and chromatin modifications), and gut microbial population changes [ 3 ]. Gut microbiota changes cause ecological dysregulation in the body, inducing colon cancer lesions through chronic inflammation.…”

Section: Introductionmentioning

confidence: 99%

Natural Products Treat Colorectal Cancer by Regulating miRNA

Guo

Geng

et al. 2023

Pharmaceuticals

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Diseases are evolving as living standards continue to improve. Cancer is the main cause of death and a major public health problem that seriously threatens human life. Colorectal cancer is one of the top ten most common malignant tumors in China, ranking second after gastric cancer among gastrointestinal malignant tumors, and its incidence rate is increasing dramatically each year due to changes in the dietary habits and lifestyle of the world’s population. Although conventional therapies, such as surgery, chemotherapy, and radiotherapy, have profoundly impacted the treatment of colorectal cancer (CRC), drug resistance and toxicity remain substantial challenges. Natural products, such as dietary therapeutic agents, are considered the safest alternative for treating CRC. In addition, there is substantial evidence that natural products can induce apoptosis, inhibit cell cycle arrest, and reduce the invasion and migration of colon cancer cells by targeting and regulating the expression and function of miRNAs. Here, we summarize the recent research findings on the miRNA-regulation-based antitumor mechanisms of various active ingredients in natural products, highlighting how natural products target miRNA regulation in colon cancer prevention and treatment. The application of natural drug delivery systems and predictive disease biomarkers in cancer prevention and treatment is also discussed. Such approaches will contribute to the discovery of new regulatory mechanisms associated with disease pathways and provide a new theoretical basis for developing novel colon cancer drugs and compounds and identifying new therapeutic targets.

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Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis

Cited by 6 publications

References 175 publications

Fast and scalable querying of eukaryotic linear motifs with gget elm

Fast and scalable querying of eukaryotic linear motifs with gget elm

Fast and scalable querying of eukaryotic linear motifs withgget elm

Natural Products Treat Colorectal Cancer by Regulating miRNA

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