Special Issue "Multidisciplinary Approaches in IBD Genetics"

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Human Genomics and Genetic Diseases".

Deadline for manuscript submissions: 15 December 2021.

Special Issue Editors

Dr. Tamas Korcsmaros
E-Mail Website
Guest Editor
Earlham Institute, Quadram Institute Bioscience, Norwich NR4 7UQ, UK
Interests: precision medicine; signaling network; host-microbe interactions; systems genomics
Dr. Johanne Brooks
E-Mail
Guest Editor
East and North Hertfordshire Trust, University of Hertfordshire, Stevenage SG1 1XR, UK
Interests: inflammatory bowel disease; systems medicine; genomics; multidisciplinary approach

Special Issue Information

Inflammatory bowel disease (IBD) is an umbrella term for a group of diseases with two subtypes: Crohn’s disease and ulcerative colitis. These diseases have a complex interplay between host genetics and the environment in terms of pathogenesis, prognosis, and response to therapies. In this Special Edition, we are focusing on the geographical genetic heterogeneity of IBD and particularly changes imparted by geographical differences in the microbiome, ethnicity, and diet to these complex diseases. Understanding the dense heterogeneity in these diseases requires multidisciplinary approaches. This Special Issue aims to provide an opportunity to pull together these different approaches to provide an insight into the pathogenesis, diagnosis, and management of these complex genetic diseases. We are therefore inviting new research papers, methodologies, and reviews in the broad fields of systems genomics, functional genomics, epigenetics, nutrigenomics, pharmacogenetics, and immunogenetics. We are also particularly interested in the role of community efforts in research in these areas.

Dr. Tamas Korcsmaros
Dr. Johanne Brooks
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Geographical variations
  • Ethnicity
  • Systems genomics
  • Functional genomics
  • Nutrigenomics
  • Microbiome
  • Pharmacogenetics
  • Immunogenetics

Published Papers (3 papers)

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Research

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Article
C3435T Polymorphism of the ABCB1 Gene in Polish Patients with Inflammatory Bowel Disease: A Case–Control and Meta-Analysis Study
Genes 2021, 12(9), 1419; https://doi.org/10.3390/genes12091419 - 15 Sep 2021
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Abstract
P-glycoprotein encoded by the ABCB1 gene constitutes a molecular barrier in the small and large bowel epithelium, and its different expression may influence susceptibility to inflammatory bowel disease (IBD). We aimed to assess the contribution of the C3435T polymorphism to disease risk in [...] Read more.
P-glycoprotein encoded by the ABCB1 gene constitutes a molecular barrier in the small and large bowel epithelium, and its different expression may influence susceptibility to inflammatory bowel disease (IBD). We aimed to assess the contribution of the C3435T polymorphism to disease risk in the Polish population. A total of 100 patients (50 Crohn’s disease (CD), 50 ulcerative colitis (UC)) and 100 healthy controls were genotyped for the single nucleotide polymorphism (SNP) C3435T by using the PCR-RFLP method. Patients were classified on the basis of disease phenotype and the specific treatment used. A meta-analysis was carried out of our results and those from previously published Polish studies. There was no significant difference in allele and genotype frequencies in IBD patients compared with controls. For CD patients, a lower frequency of TT genotype in those with colonic disease, a lower frequency of T allele, and a higher frequency of C allele in those with luminal disease were observed, whereas for UC patients, a lower frequency of CT genotype was observed in those with left-sided colitis. A meta-analysis showed a tendency towards higher prevalence of CC genotype in UC cases. These results indicate that the C3435T variants may confer a risk for UC and influence disease behaviour. Full article
(This article belongs to the Special Issue Multidisciplinary Approaches in IBD Genetics)
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Review

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Review
Role of Digital Health and Artificial Intelligence in Inflammatory Bowel Disease: A Scoping Review
Genes 2021, 12(10), 1465; https://doi.org/10.3390/genes12101465 - 22 Sep 2021
Viewed by 895
Abstract
Inflammatory bowel diseases (IBD), subdivided into Crohn’s disease (CD) and ulcerative colitis (UC), are chronic diseases that are characterized by relapsing and remitting periods of inflammation in the gastrointestinal tract. In recent years, the amount of research surrounding digital health (DH) and artificial [...] Read more.
Inflammatory bowel diseases (IBD), subdivided into Crohn’s disease (CD) and ulcerative colitis (UC), are chronic diseases that are characterized by relapsing and remitting periods of inflammation in the gastrointestinal tract. In recent years, the amount of research surrounding digital health (DH) and artificial intelligence (AI) has increased. The purpose of this scoping review is to explore this growing field of research to summarize the role of DH and AI in the diagnosis, treatment, monitoring and prognosis of IBD. A review of 21 articles revealed the impact of both AI algorithms and DH technologies; AI algorithms can improve diagnostic accuracy, assess disease activity, and predict treatment response based on data modalities such as endoscopic imaging and genetic data. In terms of DH, patients utilizing DH platforms experienced improvements in quality of life, disease literacy, treatment adherence, and medication management. In addition, DH methods can reduce the need for in-person appointments, decreasing the use of healthcare resources without compromising the standard of care. These articles demonstrate preliminary evidence of the potential of DH and AI for improving the management of IBD. However, the majority of these studies were performed in a regulated clinical environment. Therefore, further validation of these results in a real-world environment is required to assess the efficacy of these methods in the general IBD population. Full article
(This article belongs to the Special Issue Multidisciplinary Approaches in IBD Genetics)
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Review
Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications
Genes 2021, 12(9), 1438; https://doi.org/10.3390/genes12091438 - 18 Sep 2021
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Abstract
Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited [...] Read more.
Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials. Full article
(This article belongs to the Special Issue Multidisciplinary Approaches in IBD Genetics)
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