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Computational and Omics Research on Rare Diseases
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Computational and Omics Research on Rare Diseases

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 14439

Special Issue Editor

Prof. Dr. Andrea Bernini

E-Mail Website
Guest Editor
Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy
Interests: structural biology; rare diseases; metabolomics; nuclear magnetic resonance; protein dynamics; protein core & surface; transient pockets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The comprehensive measurement of the molecular state of cells allowed by omics techniques, whether singly or together in a tissue, is rapidly redefining our understanding of disease and human development. We wish to focus such an approach on rare diseases. By definition, a rare disease affects fewer than one individual in 2000 people in the general population. However, as there are nearly 8000 rare diseases, it turns out that these “rare” diseases affect more than 300 million people worldwide. Genomics and all the emerging omics techniques hold the key to new diagnoses and therapies for rare diseases, but constant advances in analytical and computational methods are needed to support these techniques at increasing scale. Innovative approaches are also desirable to translate omics research into pharmacological therapies.

We are seeking for papers that investigate rare diseases using an omics or integrated omics approach as well as computational omics involving artificial intelligence, big data, molecular simulations. Papers addressing specific pharmacological therapies involving drug-repositioning for rare diseases in light of omics studies are warmly welcomed.

Prof. Dr. Andrea Bernini
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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • rare diseases
  • genetic diseases
  • inborn
  • omics sciences
  • integrated omics approaches
  • computational omics
  • computational biology
  • bioinformatics
  • mutations
  • machine learning
  • artificial intelligence
  • molecular chaperones
  • drug repositioning
  • precision medicine

Published Papers (4 papers)

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Research

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17 pages, 3663 KiB  
Article
Alteration of the Nucleotide Excision Repair (NER) Pathway in Soft Tissue Sarcoma
by Adriano Pasqui, Anna Boddi, Domenico Andrea Campanacci, Guido Scoccianti, Andrea Bernini, Daniela Grasso, Elisabetta Gambale, Federico Scolari, Ilaria Palchetti, Annarita Palomba, Sara Fancelli, Enrico Caliman, Lorenzo Antonuzzo and Serena Pillozzi
Int. J. Mol. Sci. 2022, 23(15), 8360; https://doi.org/10.3390/ijms23158360 - 28 Jul 2022
Cited by 5 | Viewed by 4986
Abstract
Clinical responses to anticancer therapies in advanced soft tissue sarcoma (STS) are unluckily restricted to a small subgroup of patients. Much of the inter-individual variability in treatment efficacy is as result of polymorphisms in genes encoding proteins involved in drug pharmacokinetics and pharmacodynamics. [...] Read more.
Clinical responses to anticancer therapies in advanced soft tissue sarcoma (STS) are unluckily restricted to a small subgroup of patients. Much of the inter-individual variability in treatment efficacy is as result of polymorphisms in genes encoding proteins involved in drug pharmacokinetics and pharmacodynamics. The nucleotide excision repair (NER) system is the main defense mechanism for repairing DNA damage caused by carcinogens and chemotherapy drugs. Single nucleotide polymorphisms (SNPs) of NER pathway key genes, altering mRNA expression or protein activity, can be significantly associated with response to chemotherapy, toxicities, tumor relapse or risk of developing cancer. In the present study, in a cohort of STS patients, we performed DNA extraction and genotyping by SNP assay, RNA extraction and quantitative real-time reverse transcription PCR (qPCR), a molecular dynamics simulation in order to characterize the NER pathway in STS. We observed a severe deregulation of the NER pathway and we describe for the first time the effect of SNP rs1047768 in the ERCC5 structure, suggesting a role in modulating single-stranded DNA (ssDNA) binding. Our results evidenced, for the first time, the correlation between a specific genotype profile of ERCC genes and proficiency of the NER pathway in STS. Full article
(This article belongs to the Special Issue Computational and Omics Research on Rare Diseases)
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Review

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26 pages, 2669 KiB  
Review
Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology
by Daniela Grasso, Silvia Galderisi, Annalisa Santucci and Andrea Bernini
Int. J. Mol. Sci. 2023, 24(6), 5819; https://doi.org/10.3390/ijms24065819 - 18 Mar 2023
Cited by 3 | Viewed by 2053
Abstract
Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss [...] Read more.
Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss of function or improper localization/degradation. Pharmacological chaperones are small molecules restoring the correct folding of a protein suitable for treating conformational diseases. Small molecules like these bind poorly folded proteins similarly to physiological chaperones, bridging non-covalent interactions (hydrogen bonds, electrostatic interactions, and van der Waals contacts) loosened or lost due to mutations. Pharmacological chaperone development involves, among other things, structural biology investigation of the target protein and its misfolding and refolding. Such research can take advantage of computational methods at many stages. Here, we present an up-to-date review of the computational structural biology tools and approaches regarding protein stability evaluation, binding pocket discovery and druggability, drug repurposing, and virtual ligand screening. The tools are presented as organized in an ideal workflow oriented at pharmacological chaperones’ rational design, also with the treatment of rare diseases in mind. Full article
(This article belongs to the Special Issue Computational and Omics Research on Rare Diseases)
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12 pages, 608 KiB  
Review
New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches
by Samarth Thonta Setty, Marie-Pier Scott-Boyer, Tania Cuppens and Arnaud Droit
Int. J. Mol. Sci. 2022, 23(12), 6792; https://doi.org/10.3390/ijms23126792 - 18 Jun 2022
Cited by 8 | Viewed by 2980
Abstract
Rare diseases impact the lives of 300 million people in the world. Rapid advances in bioinformatics and genomic technologies have enabled the discovery of causes of 20–30% of rare diseases. However, most rare diseases have remained as unsolved enigmas to date. Newer tools [...] Read more.
Rare diseases impact the lives of 300 million people in the world. Rapid advances in bioinformatics and genomic technologies have enabled the discovery of causes of 20–30% of rare diseases. However, most rare diseases have remained as unsolved enigmas to date. Newer tools and availability of high throughput sequencing data have enabled the reanalysis of previously undiagnosed patients. In this review, we have systematically compiled the latest developments in the discovery of the genetic causes of rare diseases using machine learning methods. Importantly, we have detailed methods available to reanalyze existing whole exome sequencing data of unsolved rare diseases. We have identified different reanalysis methodologies to solve problems associated with sequence alterations/mutations, variation re-annotation, protein stability, splice isoform malfunctions and oligogenic analysis. In addition, we give an overview of new developments in the field of rare disease research using whole genome sequencing data and other omics. Full article
(This article belongs to the Special Issue Computational and Omics Research on Rare Diseases)
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14 pages, 2166 KiB  
Review
A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases
by David Pratella, Samira Ait-El-Mkadem Saadi, Sylvie Bannwarth, Véronique Paquis-Fluckinger and Silvia Bottini
Int. J. Mol. Sci. 2021, 22(19), 10891; https://doi.org/10.3390/ijms221910891 - 08 Oct 2021
Cited by 9 | Viewed by 3563
Abstract
Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficient knowledge, [...] Read more.
Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficient knowledge, many patients are not diagnosed. Nowadays, the advances in high-throughput sequencing technologies such as whole genome sequencing, single-cell and others, have boosted the understanding of RDs. To extract biological meaning using the data generated by these methods, different analysis techniques have been proposed, including machine learning algorithms. These methods have recently proven to be valuable in the medical field. Among such approaches, unsupervised learning methods via neural networks including autoencoders (AEs) or variational autoencoders (VAEs) have shown promising performances with applications on various type of data and in different contexts, from cancer to healthy patient tissues. In this review, we discuss how AEs and VAEs have been used in biomedical settings. Specifically, we discuss their current applications and the improvements achieved in diagnostic and survival of patients. We focus on the applications in the field of RDs, and we discuss how the employment of AEs and VAEs would enhance RD understanding and diagnosis. Full article
(This article belongs to the Special Issue Computational and Omics Research on Rare Diseases)
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