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Deep Sequencing in the Human Health
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Deep Sequencing in the Human Health

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Chemical Biology".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 7021

Special Issue Editors

Prof. Dr. Jianzhen Xu

E-Mail Website
Guest Editor
Computational Systems Biology Lab, Department of Bioinformatics, Shantou University Medical, Shantou, China
Interests: computational systems biology; RNA binding protein; deep sequencing
Dr. Dapeng Hao

E-Mail Website1 Website2
Guest Editor
Department of Pathology, Harbin Medical University, Harbin, China
Interests: genomic analysis; epigenome; cancer; single cell

Special Issue Information

Dear Colleagues,

Technological advancements in deep sequencing over the past decade have revolutionized molecular biology. A large amount of omics datasets have routinely been generated from a variety of platforms, such as next-generation sequencing, single-cell RNA sequencing, or long-read sequencing, which not only transform conventional biology into big data-driven research but also provide challenges for computational analysis. A key issue is how to develop novel methods to integrate the massive datasets with other knowledge to provide biological insights and translate them into clinical values.

The goal of this Special Issue is to present recent developments and novel applications in computational biology. It welcomes but is not limited to the following topics:

  • High-throughput sequencing-based studies and functional analysis of biomolecules (DNA, protein, noncoding RNA, etc.) in mammalian systems;
  • Application of omics approaches in translational medicine, such as target identification and biomarker discovery;
  • Integration of omics data with biomedical literature and knowledge ontology for molecular interaction network analysis;
  • Single cell analysis to elucidate the microenvironment of cancer and biological systems;
  • Novel computational methods and pipelines for sequencing data analysis.

We welcome all article types, including research articles, review articles, as well as short communications. All data and codes must be available (e.g., deposited in public domain or accessed freely).

Prof. Dr. Jianzhen Xu
Dr. Dapeng Hao
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 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. Molecules is an international peer-reviewed open access semimonthly 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 2700 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

  • deep sequencing
  • single-cell sequencing
  • omics data
  • target identification
  • biomarker
  • molecular network
  • biomedical literature mining
  • knowledge ontology

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Published Papers (3 papers)

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Research

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18 pages, 4733 KiB  
Article
MOBT Alleviates Pulmonary Fibrosis through an lncITPF–hnRNP-l-Complex-Mediated Signaling Pathway
by Pan Xu, Haitong Zhang, Huangting Li, Bo Liu, Rongrong Li, Jinjin Zhang, Xiaodong Song, Changjun Lv, Hongbo Li and Mingwei Chen
Molecules 2022, 27(16), 5336; https://doi.org/10.3390/molecules27165336 - 22 Aug 2022
Cited by 3 | Viewed by 2363
Abstract
Pulmonary fibrosis is characterized by the destruction of alveolar architecture and the irreversible scarring of lung parenchyma, with few therapeutic options and effective therapeutic drugs. Here, we demonstrate the anti-pulmonary fibrosis of 3-(4-methoxyphenyl)-4-oxo-4H-1-benzopyran-7-yl(αS)-α,3,4-trihydroxybenzenepropanoate (MOBT) in mice and a cell model induced [...] Read more.
Pulmonary fibrosis is characterized by the destruction of alveolar architecture and the irreversible scarring of lung parenchyma, with few therapeutic options and effective therapeutic drugs. Here, we demonstrate the anti-pulmonary fibrosis of 3-(4-methoxyphenyl)-4-oxo-4H-1-benzopyran-7-yl(αS)-α,3,4-trihydroxybenzenepropanoate (MOBT) in mice and a cell model induced by bleomycin and transforming growth factor-β1. The anti-pulmonary fibrosis of MOBT was evaluated using a MicroCT imaging system for small animals, lung function analysis and H&E and Masson staining. The results of RNA fluorescence in situ hybridization, chromatin immunoprecipitation (ChIP)-PCR, RNA immunoprecipitation, ChIP-seq, RNA-seq, and half-life experiments demonstrated the anti-pulmonary fibrotic mechanism. Mechanistic dissection showed that MOBT inhibited lncITPF transcription by preventing p-Smad2/3 translocation from the cytoplasm to the nucleus, resulting in a reduction in the amount of the lncITPF–hnRNP L complex. The decreased lncITPF–hnRNP L complex reduced MEF2c expression by blocking its alternative splicing, which in turn inhibited the expression of MEF2c target genes, such as TAGLN2 and FMN1. Briefly, MOBT alleviated pulmonary fibrosis through the lncITPF–hnRNP-l-complex-targeted MEF2c signaling pathway. We hope that this study will provide not only a new drug candidate but also a novel therapeutic drug target, which will bring new treatment strategies for pulmonary fibrosis. Full article
(This article belongs to the Special Issue Deep Sequencing in the Human Health)
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25 pages, 6901 KiB  
Article
Prognostic Ability of Enhancer RNAs in Metastasis of Non-Small Cell Lung Cancer
by Jun Liu, Jingyi Jia, Siqiao Wang, Junfang Zhang, Shuyuan Xian, Zixuan Zheng, Lin Deng, Yonghong Feng, Yuan Zhang and Jie Zhang
Molecules 2022, 27(13), 4108; https://doi.org/10.3390/molecules27134108 - 26 Jun 2022
Cited by 4 | Viewed by 2515
Abstract
(1) Background: Non-small cell lung cancer (NSCLC) is the most common lung cancer. Enhancer RNA (eRNA) has potential utility in the diagnosis, prognosis and treatment of cancer, but the role of eRNAs in NSCLC metastasis is not clear; (2) Methods: Differentially expressed transcription [...] Read more.
(1) Background: Non-small cell lung cancer (NSCLC) is the most common lung cancer. Enhancer RNA (eRNA) has potential utility in the diagnosis, prognosis and treatment of cancer, but the role of eRNAs in NSCLC metastasis is not clear; (2) Methods: Differentially expressed transcription factors (DETFs), enhancer RNAs (DEEs), and target genes (DETGs) between primary NSCLC and metastatic NSCLC were identified. Prognostic DEEs (PDEEs) were screened by Cox regression analyses and a predicting model for metastatic NSCLC was constructed. We identified DEE interactions with DETFs, DETGs, reverse phase protein arrays (RPPA) protein chips, immunocytes, and pathways to construct a regulation network using Pearson correlation. Finally, the mechanisms and clinical significance were explained using multi-dimensional validation unambiguously; (3) Results: A total of 255 DEEs were identified, and 24 PDEEs were selected into the multivariate Cox regression model (AUC = 0.699). Additionally, the NSCLC metastasis-specific regulation network was constructed, and six key PDEEs were defined (ANXA8L1, CASTOR2, CYP4B1, GTF2H2C, PSMF1 and TNS4); (4) Conclusions: This study focused on the exploration of the prognostic value of eRNAs in the metastasis of NSCLC. Finally, six eRNAs were identified as potential markers for the prediction of metastasis of NSCLC. Full article
(This article belongs to the Special Issue Deep Sequencing in the Human Health)
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Review

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11 pages, 1124 KiB  
Review
Literature Mining of Disease Associated Noncoding RNA in the Omics Era
by Jian Fan
Molecules 2022, 27(15), 4710; https://doi.org/10.3390/molecules27154710 - 23 Jul 2022
Cited by 2 | Viewed by 1702
Abstract
Noncoding RNAs (ncRNA) are transcripts without protein-coding potential that play fundamental regulatory roles in diverse cellular processes and diseases. The application of deep sequencing experiments in ncRNA research have generated massive omics datasets, which require rapid examination, interpretation and validation based on exiting [...] Read more.
Noncoding RNAs (ncRNA) are transcripts without protein-coding potential that play fundamental regulatory roles in diverse cellular processes and diseases. The application of deep sequencing experiments in ncRNA research have generated massive omics datasets, which require rapid examination, interpretation and validation based on exiting knowledge resources. Thus, text-mining methods have been increasingly adapted for automatic extraction of relations between an ncRNA and its target or a disease condition from biomedical literature. These bioinformatics tools can also assist in more complex research, such as database curation of candidate ncRNAs and hypothesis generation with respect to pathophysiological mechanisms. In this concise review, we first introduced basic concepts and workflow of literature mining systems. Then, we compared available bioinformatics tools tailored for ncRNA studies, including the tasks, applicability, and limitations. Their powerful utilities and flexibility are demonstrated by examples in a variety of diseases, such as Alzheimer’s disease, atherosclerosis and cancers. Finally, we outlined several challenges from the viewpoints of both system developers and end users. We concluded that the application of text-mining techniques will booster disease-associated ncRNA discoveries in the biomedical literature and enable integrative biology in the current omics era. Full article
(This article belongs to the Special Issue Deep Sequencing in the Human Health)
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