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A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 14849

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Special Issue Editor

Dr. Nuno S. Osório

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Guest Editor

Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus Gualtar, 4710-057 Braga, Portugal
Interests: translational bioinformatics; genomics; tuberculosis; HIV; SARS-CoV-2
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, biomedical research with translational potential gradually changed from the investigation of limited sets of biological elements to a broader, more general, and complete perspective. The vital factors in this transition were remarkable technological developments in sequencing and related technologies, sample preparation methods, and bioinformatics tools. The ongoing revolution in this area enabled multiomics approaches, the sequencing of nucleic acids at single-cell resolutions, among others. In parallel, the potential for the use of publicly available “omics” datasets in integrative large-scale analysis is ever-increasing. Collectively, this raises the possibility of undertaking research, considering near full complexity of biological systems, that can be translated into advanced diagnostics, medical procedures, and therapies.

In this Special Issue of Genes, we invite you to send your contributions concerning any aspects related to the use of genomics, transcriptomics, proteomics, metabolomics, or other “omics” in fundamental or applied human infectious diseases studies. Submissions may range from genetic to metabolic and evolutionary aspects of host–pathogen interactions, including transcriptomic, genetic or epigenetic changes related to immune system responses or antimicrobial resistance in major, emerging or neglected human infectious diseases.

Dr. Nuno S. Osório
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

genomics
transcriptomics
metabolomics
infectious diseases
omics
bioinformatics
host–pathogen interaction

Published Papers (5 papers)

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Research

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15 pages, 3548 KiB

Open AccessArticle

Research on Potential Network Markers and Signaling Pathways in Type 2 Diabetes Based on Conditional Cell-Specific Network

by Yuke Xie, Zhizhong Cui, Nan Wang and Peiluan Li

Genes 2022, 13(7), 1155; https://0-doi-org.brum.beds.ac.uk/10.3390/genes13071155 - 26 Jun 2022

Viewed by 1739

Abstract

Traditional methods concerning type 2 diabetes (T2D) are limited to grouped cells instead of each single cell, and thus the heterogeneity of single cells is erased. Therefore, it is still challenging to study T2D based on a single-cell and network perspective. In this study, we construct a conditional cell-specific network (CCSN) for each single cell for the GSE86469 dataset which is a single-cell transcriptional set from nondiabetic (ND) and T2D human islet samples, and obtain a conditional network degree matrix (CNDM). Since beta cells are the key cells leading to T2D, we search for hub genes in CCSN of beta cells and find that ATP6AP2 is essential for regulation and storage of insulin, and the renin-angiotensin system involving ATP6AP2 is related to most pathological processes leading to diabetic nephropathy. The communication between beta cells and other endocrine cells is performed and three gene pairs with obvious interaction are found. In addition, different expression genes (DEGs) are found based on CNDM and the gene expression matrix (GEM), respectively. Finally, ‘dark’ genes are identified, and enrichment analysis shows that NFATC2 is involved in the VEGF signaling pathway and indirectly affects the production of Prostacyclin (PGI₂), which may be a potential biomarker for diabetic nephropathy. Full article

(This article belongs to the Special Issue From Basic to Translational Bioinformatics of Human Infectious Diseases)

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10 pages, 2035 KiB

Open AccessArticle

HIV Protease and Integrase Empirical Substitution Models of Evolution: Protein-Specific Models Outperform Generalist Models

by Roberto Del Amparo and Miguel Arenas

Genes 2022, 13(1), 61; https://0-doi-org.brum.beds.ac.uk/10.3390/genes13010061 - 27 Dec 2021

Cited by 6 | Viewed by 2237

Abstract

Diverse phylogenetic methods require a substitution model of evolution that should mimic, as accurately as possible, the real substitution process. At the protein level, empirical substitution models have traditionally been based on a large number of different proteins from particular taxonomic levels. However, these models assume that all of the proteins of a taxonomic level evolve under the same substitution patterns. We believe that this assumption is highly unrealistic and should be relaxed by considering protein-specific substitution models that account for protein-specific selection processes. In order to test this hypothesis, we inferred and evaluated four new empirical substitution models for the protease and integrase of HIV and other viruses. We found that these models more accurately fit, compared with any of the currently available empirical substitution models, the evolutionary process of these proteins. We conclude that evolutionary inferences from protein sequences are more accurate if they are based on protein-specific substitution models rather than taxonomic-specific (generalist) substitution models. We also present four new empirical substitution models of protein evolution that could be useful for phylogenetic inferences of viral protease and integrase. Full article

(This article belongs to the Special Issue From Basic to Translational Bioinformatics of Human Infectious Diseases)

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12 pages, 492 KiB

Open AccessArticle

Characterizing HIV-1 Genetic Subtypes and Drug Resistance Mutations among Children, Adolescents and Pregnant Women in Sierra Leone

by George A. Yendewa, Sulaiman Lakoh, Sahr A. Yendewa, Khadijah Bangura, Andrés Tabernilla, Lucia Patiño, Darlinda F. Jiba, Alren O. Vandy, Samuel P. Massaquoi, Nuno S. Osório, Gibrilla F. Deen, Foday Sahr, Robert A. Salata and Eva Poveda

Genes 2021, 12(9), 1314; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12091314 - 26 Aug 2021

Cited by 3 | Viewed by 2270

Abstract

Human immunodeficiency virus (HIV) drug resistance (HIVDR) is widespread in sub-Saharan Africa. Children and pregnant women are particularly vulnerable, and laboratory testing capacity remains limited. We, therefore, used a cross-sectional design and convenience sampling to characterize HIV subtypes and resistance-associated mutations (RAMs) in these groups in Sierra Leone. In total, 96 children (age 2–9 years, 100% ART-experienced), 47 adolescents (age 10–18 years, 100% ART-experienced), and 54 pregnant women (>18 years, 72% ART-experienced) were enrolled. Median treatment durations were 36, 84, and 3 months, respectively, while the sequencing success rates were 45%, 70%, and 59%, respectively, among children, adolescents, and pregnant women. Overall, the predominant HIV-1 subtype was CRF02_AG (87.9%, 95/108), with minority variants constituting 12%. Among children and adolescents, the most common RAMs were M184V (76.6%, n = 49/64), K103N (45.3%, n = 29/64), Y181C/V/I (28.1%, n = 18/64), T215F/Y (25.0%, n = 16/64), and V108I (18.8%, n = 12/64). Among pregnant women, the most frequent RAMs were K103N (20.6%, n = 7/34), M184V (11.8%, n = 4/34), Y181C/V/I (5.9%, n = 2/34), P225H (8.8%, n = 3/34), and K219N/E/Q/R (5.9%, n = 2/34). Protease and integrase inhibitor-RAMs were relatively few or absent. Based on the genotype susceptibility score distributions, 73%, 88%, and 14% of children, adolescents, and pregnant women, respectively, were not susceptible to all three drug components of the WHO preferred first-line regimens per 2018 guidelines. These findings suggest that routine HIVDR surveillance and access to better ART choices may improve treatment outcomes in Sierra Leone. Full article

(This article belongs to the Special Issue From Basic to Translational Bioinformatics of Human Infectious Diseases)

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12 pages, 3867 KiB

Open AccessArticle

Bioinformatic Analysis of Two TOR (Target of Rapamycin)-Like Proteins Encoded by Entamoeba histolytica Revealed Structural Similarities with Functional Homologs

by Patricia L. A. Muñoz-Muñoz, Rosa E. Mares-Alejandre, Samuel G. Meléndez-López and Marco A. Ramos-Ibarra

Genes 2021, 12(8), 1139; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12081139 - 28 Jul 2021

Cited by 1 | Viewed by 1817

Abstract

The target of rapamycin (TOR), also known as FKBP-rapamycin associated protein (FRAP), is a protein kinase belonging to the PIKK (phosphatidylinositol 3-kinase (PI3K)-related kinases) family. TOR kinases are involved in several signaling pathways that control cell growth and proliferation. Entamoeba histolytica, the protozoan parasite that causes human amoebiasis, contains two genes encoding TOR-like proteins: EhFRAP and EhTOR2. To assess their potential as drug targets to control the cell proliferation of E. histolytica, we studied the structural features of EhFRAP and EhTOR2 using a biocomputational approach. The overall results confirmed that both TOR amoebic homologs share structural similarities with functional TOR kinases, and show inherent abilities to form TORC complexes and participate in protein-protein interaction networks. To our knowledge, this study represents the first in silico characterization of the structure-function relationships of EhFRAP and EhTOR2. Full article

(This article belongs to the Special Issue From Basic to Translational Bioinformatics of Human Infectious Diseases)

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Review

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16 pages, 32568 KiB

Open AccessReview

The Neglected Contribution of Streptomycin to the Tuberculosis Drug Resistance Problem

by Deisy M. G. C. Rocha, Miguel Viveiros, Margarida Saraiva and Nuno S. Osório

Genes 2021, 12(12), 2003; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12122003 - 17 Dec 2021

Cited by 4 | Viewed by 5926

Abstract

The airborne pathogen Mycobacterium tuberculosis is responsible for a present major public health problem worsened by the emergence of drug resistance. M. tuberculosis has acquired and developed streptomycin (STR) resistance mechanisms that have been maintained and transmitted in the population over the last decades. Indeed, STR resistant mutations are frequently identified across the main M. tuberculosis lineages that cause tuberculosis outbreaks worldwide. The spread of STR resistance is likely related to the low impact of the most frequent underlying mutations on the fitness of the bacteria. The withdrawal of STR from the first-line treatment of tuberculosis potentially lowered the importance of studying STR resistance. However, the prevalence of STR resistance remains very high, could be underestimated by current genotypic methods, and was found in outbreaks of multi-drug (MDR) and extensively drug (XDR) strains in different geographic regions. Therefore, the contribution of STR resistance to the problem of tuberculosis drug resistance should not be neglected. Here, we review the impact of STR resistance and detail well-known and novel candidate STR resistance mechanisms, genes, and mutations. In addition, we aim to provide insights into the possible role of STR resistance in the development of multi-drug resistant tuberculosis. Full article

(This article belongs to the Special Issue From Basic to Translational Bioinformatics of Human Infectious Diseases)

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