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Translational Bioinformatics: From Prediction to Validation
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Translational Bioinformatics: From Prediction to Validation

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 13281

Special Issue Editor

Dr. Raya Khanin

E-Mail Website
Guest Editor
1.Bioinformatics Core, Memorial Sloan Kettering Cancer Center, New York, New York, NY 10065, USA
2.Co-founder and CSO, LifeNome Inc., NY 10036, USA
Interests: computational genomics; translational bioinformatics; precision medicine

Special Issue Information

Dear Colleagues,

Incredible advancements in genomics technologies are driving towards a more comprehensive understanding of how our bodies function in health and disease. There is a growing acceptance that the “one-size-fits-all” approach in healthcare needs to be replaced by precision medicine and precision health.

With the plummeting costs of genotyping, government-led programs, such as UK Biobank and “All of us” in the US, are collecting large-scale biological data and deep phenotypic, environmental, and behavioral data on hundreds of thousands, and even millions, of people. Private DNA testing companies have already amassed genetic data on over 26 million people. Turning this rapidly growing multi-dimensional data into precise insights and enabling a new era of personalized health requires AI-powered integration of physiological, nutrition, exercise, and wellness programs.

Numerous genomics-based discoveries have already been made, from developing polygenic prediction risk scores for complex chronic diseases to individualized responses to foods, genetic predispositions to vitamin imbalances, and biomarkers of aging. Still, many challenges remain: from devising integration approaches that harness rapidly growing data from multiple sources to translating this dynamic knowledge into actionable insights and lifestyle solutions for consumers.

This Special Issue calls for original research papers, reviews, and case studies that address implementation of genomics-based approaches to early chronic disease detection and prevention, risk mitigation by personalized diet and lifestyle changes, as well as validation case-studies. Topics of interest include integration of biological data (DNA, microbiome) with wearables and mobile devices, and development of precision health and precision nutrition platforms.

Dr. Raya Khanin
Guest Editor

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 submissions that pass pre-check are 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 2600 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

  • Precision health
  • Nutrigenomics
  • Translational bioinformatics
  • Personalized medicine
  • Precision medicine
  • Polygenic risk scores
  • Genomics biomarkers

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Published Papers (3 papers)

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Research

12 pages, 1507 KiB  
Article
Exome-Wide Analysis of the DiscovEHR Cohort Reveals Novel Candidate Pharmacogenomic Variants for Clinical Pharmacogenomics
by Maria-Theodora Pandi, Marc S. Williams, Peter van der Spek, Maria Koromina and George P. Patrinos
Genes 2020, 11(5), 561; https://doi.org/10.3390/genes11050561 - 18 May 2020
Cited by 4 | Viewed by 3423
Abstract
Recent advances in next-generation sequencing technology have led to the production of an unprecedented volume of genomic data, thus further advancing our understanding of the role of genetic variation in clinical pharmacogenomics. In the present study, we used whole exome sequencing data from [...] Read more.
Recent advances in next-generation sequencing technology have led to the production of an unprecedented volume of genomic data, thus further advancing our understanding of the role of genetic variation in clinical pharmacogenomics. In the present study, we used whole exome sequencing data from 50,726 participants, as derived from the DiscovEHR cohort, to identify pharmacogenomic variants of potential clinical relevance, according to their occurrence within the PharmGKB database. We further assessed the distribution of the identified rare and common pharmacogenomics variants amongst different GnomAD subpopulations. Overall, our findings show that the use of publicly available sequence data, such as the DiscovEHR dataset and GnomAD, provides an opportunity for a deeper understanding of genetic variation in pharmacogenes with direct implications in clinical pharmacogenomics. Full article
(This article belongs to the Special Issue Translational Bioinformatics: From Prediction to Validation)
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15 pages, 1937 KiB  
Article
Differentially Expressed Genes of Natural Killer Cells Can Distinguish Rheumatoid Arthritis Patients from Healthy Controls
by Noha Mousaad Elemam, Mahmood Yaseen Hachim, Suad Hannawi and Azzam A. Maghazachi
Genes 2020, 11(5), 492; https://doi.org/10.3390/genes11050492 - 30 Apr 2020
Cited by 17 | Viewed by 3734
Abstract
Rheumatoid arthritis (RA) is one of the most prevalent autoimmune diseases, while its molecular triggers are not fully understood. A few studies have shown that natural killer (NK) cells may play either a pathogenic or a protective role in RA. In this study, [...] Read more.
Rheumatoid arthritis (RA) is one of the most prevalent autoimmune diseases, while its molecular triggers are not fully understood. A few studies have shown that natural killer (NK) cells may play either a pathogenic or a protective role in RA. In this study, we sought to explore NK cell markers that could be plausibly used in evaluating the differences among healthy controls and RA patients. Publicly available transcriptome datasets from RA patients and healthy volunteers were analyzed, in order to identify differentially expressed genes (DEGs) between 1. different immune cells as compared to NK cells, and 2. NK cells of RA patients and healthy controls. The identified DEGs were validated using 16 healthy controls and 17 RA patients. Peripheral blood mononuclear cells (PBMCs) were separated by Ficoll density gradient method, while NK cells were isolated using RosetteSep technique. RNA was extracted and gene expression was assessed using RT-qPCR. All selected genes were differentially expressed in NK cells compared to PBMCs. CD56, CXCL16, PECAM-1, ITGB7, BTK, TLR10, and IL-1β were significantly upregulated, while CCL2, CCR4, RELA and IBTK were downregulated in the NK cells of RA patients when compared to healthy controls. Therefore, these NK specific genes might be used as promising biomarkers for RA diagnosis. Full article
(This article belongs to the Special Issue Translational Bioinformatics: From Prediction to Validation)
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16 pages, 3287 KiB  
Article
An Integrative Phenotype–Genotype Approach Using Phenotypic Characteristics from the UAE National Diabetes Study Identifies HSD17B12 as a Candidate Gene for Obesity and Type 2 Diabetes
by Mahmood Y. Hachim, Hayat Aljaibeji, Rifat A. Hamoudi, Ibrahim Y. Hachim, Noha M. Elemam, Abdul Khader Mohammed, Albert Salehi, Jalal Taneera and Nabil Sulaiman
Genes 2020, 11(4), 461; https://doi.org/10.3390/genes11040461 - 23 Apr 2020
Cited by 15 | Viewed by 5571
Abstract
The United Arab Emirates National Diabetes and Lifestyle Study (UAEDIAB) has identified obesity, hypertension, obstructive sleep apnea, and dyslipidemia as common phenotypic characteristics correlated with diabetes mellitus status. As these phenotypes are usually linked with genetic variants, we hypothesized that these phenotypes share [...] Read more.
The United Arab Emirates National Diabetes and Lifestyle Study (UAEDIAB) has identified obesity, hypertension, obstructive sleep apnea, and dyslipidemia as common phenotypic characteristics correlated with diabetes mellitus status. As these phenotypes are usually linked with genetic variants, we hypothesized that these phenotypes share single nucleotide polymorphism (SNP)-clusters that can be used to identify causal genes for diabetes. We explored the National Human Genome Research Institute-European Bioinformatics Institute Catalog of Published Genome-Wide Association Studies (NHGRI-EBI GWAS) to list SNPs with documented association with the UAEDIAB-phenotypes as well as diabetes. The shared chromosomal regions affected by SNPs were identified, intersected, and searched for Enriched Ontology Clustering. The potential SNP-clusters were validated using targeted DNA next-generation sequencing (NGS) in two Emirati diabetic patients. RNA sequencing from human pancreatic islets was used to study the expression of identified genes in diabetic and non-diabetic donors. Eight chromosomal regions containing 46 SNPs were identified in at least four out of the five UAEDIAB-phenotypes. A list of 34 genes was shown to be affected by those SNPs. Targeted NGS from two Emirati patients confirmed that the identified genes have similar SNP-clusters. ASAH1, LRP4, FES, and HSD17B12 genes showed the highest SNPs rate among the identified genes. RNA-seq analysis revealed high expression levels of HSD17B12 in human islets and to be upregulated in type 2 diabetes (T2D) donors. Our integrative phenotype-genotype approach is a novel, simple, and powerful tool to identify clinically relevant potential biomarkers in diabetes. HSD17B12 is a novel candidate gene for pancreatic β-cell function. Full article
(This article belongs to the Special Issue Translational Bioinformatics: From Prediction to Validation)
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