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Integration and Understanding of the Regulatory System of Human Disease
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Integration and Understanding of the Regulatory System of Human Disease

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 (15 October 2023) | Viewed by 5645

Special Issue Editor

Prof. Dr. Ândrea Ribeiro-dos-Santos

E-Mail Website
Guest Editor
Instituto de Ciências Biológicas, Universidade Federal do Pará, Belem, Brazil
Interests: genetics, bioinformatics; circular RNA, circRNA; CIRI program

Special Issue Information

Dear Colleagues,

We wish to build an integrated view of the regulatory mechanisms involved in the mechanisms of illness (pathologic processes) through the complex connections of interactions between different biological networks (genomics, transcriptome, epigenomics and proteomics) and their metabolic and immunological responses. These analyses will be carried out from the perspective of the integration of biological data—mainly on a genomic scale—and computational tools.

  • Reconstruction of regulatory network;
  • Immunogenetics of diases;
  • Bioinformatic applied to population genetics;
  • Human and medical genetics;
  • Genomic;
  • Epigenomic;
  • Regulatory mechanisms involved in the illness process;
  • Microbiome;
  • Computational biology;
  • Integration and mining of biological data on a genomic scale.

Prof. Dr. Ândrea Ribeiro-dos-Santos
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

  • human and medical genetics
  • genomic
  • epigenomic
  • regulatory mechanisms
  • microbiome
  • computational biology, web tools, integration and mining of biological data on a genomic scale
  • population genetics

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Published Papers (3 papers)

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Research

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19 pages, 1986 KiB  
Article
scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets
by Madison Dautle, Shaoqiang Zhang and Yong Chen
Int. J. Mol. Sci. 2023, 24(17), 13339; https://doi.org/10.3390/ijms241713339 - 28 Aug 2023
Cited by 2 | Viewed by 2058
Abstract
Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designed to predict GRNs from scRNA-seq data, they usually have high false positive [...] Read more.
Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designed to predict GRNs from scRNA-seq data, they usually have high false positive rates and none infer GRNs by directly using the paired datasets of case-versus-control experiments. Here we present a novel deep-learning-based method, named scTIGER, for GRN detection by using the co-differential relationships of gene expression profiles in paired scRNA-seq datasets. scTIGER employs cell-type-based pseudotiming, an attention-based convolutional neural network method and permutation-based significance testing for inferring GRNs among gene modules. As state-of-the-art applications, we first applied scTIGER to scRNA-seq datasets of prostate cancer cells, and successfully identified the dynamic regulatory networks of AR, ERG, PTEN and ATF3 for same-cell type between prostatic cancerous and normal conditions, and two-cell types within the prostatic cancerous environment. We then applied scTIGER to scRNA-seq data from neurons with and without fear memory and detected specific regulatory networks for BDNF, CREB1 and MAPK4. Additionally, scTIGER demonstrates robustness against high levels of dropout noise in scRNA-seq data. Full article
(This article belongs to the Special Issue Integration and Understanding of the Regulatory System of Human Disease)
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11 pages, 1787 KiB  
Article
Bact-to-Batch: A Microbiota-Based Tool to Determine Optimal Animal Allocation in Experimental Designs
by Gaël Even, Anthony Mouray, Nicolas Vandenabeele, Sophie Martel, Sophie Merlin, Ségolène Lebrun-Ruer, Magali Chabé and Christophe Audebert
Int. J. Mol. Sci. 2023, 24(9), 7912; https://doi.org/10.3390/ijms24097912 - 26 Apr 2023
Cited by 2 | Viewed by 1845
Abstract
The basis of any animal experimentation begins with the housing of animals that should take into account the need for splitting animals into similar groups. Even if it is generally recommended to use the minimum number of animals necessary to obtain reliable and [...] Read more.
The basis of any animal experimentation begins with the housing of animals that should take into account the need for splitting animals into similar groups. Even if it is generally recommended to use the minimum number of animals necessary to obtain reliable and statistically significant results (3Rs rule), the allocation of animals is currently mostly based on randomness. Since variability in gut microbiota is an important confounding factor in animal experiments, the main objective of this study was to develop a new approach based on 16S rRNA gene sequencing analysis of the gut microbiota of animals participating in an experiment, in order to correctly assign the animals across batches. For this purpose, a pilot study was performed on 20 mouse faecal samples with the aim of establishing two groups of 10 mice as similar as possible in terms of their faecal microbiota fingerprinting assuming that this approach limits future analytical bias and ensures reproducibility. The suggested approach was challenged with previously published data from a third-party study. This new method allows to embrace the unavoidable microbiota variability between animals in order to limit artefacts and to provide an additional assurance for the reproducibility of animal experiments. Full article
(This article belongs to the Special Issue Integration and Understanding of the Regulatory System of Human Disease)
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Review

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14 pages, 916 KiB  
Review
Independent and Interactive Roles of Immunity and Metabolism in Aortic Dissection
by Siyu Li, Jun Li, Wei Cheng, Wenhui He and Shuang-Shuang Dai
Int. J. Mol. Sci. 2023, 24(21), 15908; https://doi.org/10.3390/ijms242115908 - 2 Nov 2023
Cited by 3 | Viewed by 1373
Abstract
Aortic dissection (AD) is a cardiovascular disease that seriously endangers the lives of patients. The mortality rate of this disease is high, and the incidence is increasing annually, but the pathogenesis of AD is complicated. In recent years, an increasing number of studies [...] Read more.
Aortic dissection (AD) is a cardiovascular disease that seriously endangers the lives of patients. The mortality rate of this disease is high, and the incidence is increasing annually, but the pathogenesis of AD is complicated. In recent years, an increasing number of studies have shown that immune cell infiltration in the media and adventitia of the aorta is a novel hallmark of AD. These cells contribute to changes in the immune microenvironment, which can affect their own metabolism and that of parenchymal cells in the aortic wall, which are essential factors that induce degeneration and remodeling of the vascular wall and play important roles in the formation and development of AD. Accordingly, this review focuses on the independent and interactive roles of immunity and metabolism in AD to provide further insights into the pathogenesis, novel ideas for diagnosis and new strategies for treatment or early prevention of AD. Full article
(This article belongs to the Special Issue Integration and Understanding of the Regulatory System of Human Disease)
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