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Special Issue "OMICs, Data Integration, and Applications in Personalized Medicine"

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 (28 February 2021).

Special Issue Editors

Prof. Dr. Sergio Crovella
E-Mail Website
Guest Editor
Department of Biological and Environmental Sciences, College of Arts and Sciences, Qatar University, Doha, Qatar
Interests: Immunology; clinical pathology; molecular genetics; molecular biology; OMICs; bioinformatics
Special Issues and Collections in MDPI journals
Prof. Dr. Lucas Brandao
E-Mail Website
Guest Editor
Department of Pathology, Federal University of Pernambuco, Recife, Brazil
Interests: molecular pathology; bioinformatics; molecular immunology; OMICs; histopathology; immunogenetics; human molecular genetics

Special Issue Information

Dear Colleagues,

OMICs have allowed clinical researchers to partially unravel the complexity of molecular interaction networks and highlight the main target molecules that are usually associated with disease onset. Nevertheless, few studies that aim to integrate OMICs data for the purpose of unravelling the molecular basis of diseases have been published so far. To date, each OMIC has only been considered in isolation in a unique clinical context. Therefore, molecular data have accumulated in databases, specific repositories, and the literature that have not been analyzed globally with the aim of producing integrated findings.

Many of the scientific responses that have already been produced have not been understood and interpreted at a coherent biological level as they have not been integrated.

This Special Issue is dedicated to OMICs data integration for the purpose of unravelling the molecular pathogenesis of diseases.

Both original research articles and reviews on this subject are welcome.

Prof. Dr. Sergio Crovella
Prof. Dr. Lucas Brandao
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 papers will be 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

  • OMICs
  • molecular markers
  • molecular pathology
  • big data integration
  • precision medicine
  • bioinformatics

Published Papers (8 papers)

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Research

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Article
Calreticulin Deficiency Disturbs Ribosome Biogenesis and Results in Retardation in Embryonic Kidney Development
Int. J. Mol. Sci. 2021, 22(11), 5858; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22115858 - 30 May 2021
Viewed by 847
Abstract
Nephrogenesis is driven by complex signaling pathways that control cell growth and differentiation. The endoplasmic reticulum chaperone calreticulin (Calr) is well known for its function in calcium storage and in the folding of glycoproteins. Its role in kidney development is still not understood. [...] Read more.
Nephrogenesis is driven by complex signaling pathways that control cell growth and differentiation. The endoplasmic reticulum chaperone calreticulin (Calr) is well known for its function in calcium storage and in the folding of glycoproteins. Its role in kidney development is still not understood. We provide evidence for a pivotal role of Calr in nephrogenesis in this investigation. We show that Calr deficiency results in the disrupted formation of an intact nephrogenic zone and in retardation of nephrogenesis, as evidenced by the disturbance in the formation of comma-shaped and s-shaped bodies. Using proteomics and transcriptomics approaches, we demonstrated that in addition to an alteration in Wnt-signaling key proteins, embryonic kidneys from Calr−/− showed an overall impairment in expression of ribosomal proteins which reveals disturbances in protein synthesis and nephrogenesis. CRISPR/cas9 mediated knockout confirmed that Calr deficiency is associated with a deficiency of several ribosomal proteins and key proteins in ribosome biogenesis. Our data highlights a direct link between Calr expression and the ribosome biogenesis. Full article
(This article belongs to the Special Issue OMICs, Data Integration, and Applications in Personalized Medicine)
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Article
Transcriptional Profiling of Whisker Follicles and of the Striatum in Methamphetamine Self-Administered Rats
Int. J. Mol. Sci. 2020, 21(22), 8856; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21228856 - 23 Nov 2020
Cited by 1 | Viewed by 748
Abstract
Methamphetamine (MA) use disorder is a chronic neuropsychiatric disease characterized by recurrent binge episodes, intervals of abstinence, and relapses to MA use. Therefore, identification of the key genes and pathways involved is important for improving the diagnosis and treatment of this disorder. In [...] Read more.
Methamphetamine (MA) use disorder is a chronic neuropsychiatric disease characterized by recurrent binge episodes, intervals of abstinence, and relapses to MA use. Therefore, identification of the key genes and pathways involved is important for improving the diagnosis and treatment of this disorder. In this study, high-throughput RNA sequencing was performed to find the key genes and examine the comparability of gene expression between whisker follicles and the striatum of rats following MA self-administration. A total of 253 and 87 differentially expressed genes (DEGs) were identified in whisker follicles and the striatum, respectively. Multivariate and network analyses were performed on these DEGs to find hub genes and key pathways within the constructed network. A total of 129 and 49 genes were finally selected from the DEG sets of whisker follicles and of the striatum. Statistically significant DEGs were found to belong to the classes of genes involved in nicotine addiction, cocaine addiction, and amphetamine addiction in the striatum as well as in Parkinson’s, Huntington’s, and Alzheimer’s diseases in whisker follicles. Of note, several genes and pathways including retrograde endocannabinoid signaling and the synaptic vesicle cycle pathway were common between the two tissues. Therefore, this study provides the first data on gene expression levels in whisker follicles and in the striatum in relation to MA reward and thereby may accelerate the research on the whisker follicle as an alternative source of biomarkers for the diagnosis of MA use disorder. Full article
(This article belongs to the Special Issue OMICs, Data Integration, and Applications in Personalized Medicine)
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Article
Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components
Int. J. Mol. Sci. 2020, 21(21), 8202; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21218202 - 02 Nov 2020
Cited by 1 | Viewed by 730
Abstract
The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most [...] Read more.
The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analysis. This would solve the limitations of current methods by applying variable clustering and sparse principal component (sPC) analysis. Our approach consists of two stages. The first stage identifies homogeneous variable blocks, and then extracts sPCs, for each omics dataset. The second stage merges sPCs from each omics dataset, and then constructs a prediction model. We also propose a graphical method showing the results of sparse PCA and model fitting, simultaneously. We applied the proposed methodology to glioblastoma multiforme data from The Cancer Genome Atlas. The comparison with other existing approaches showed that our proposed methodology is more easily interpretable than other approaches, and has comparable predictive power, with a much smaller number of variables. Full article
(This article belongs to the Special Issue OMICs, Data Integration, and Applications in Personalized Medicine)
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Article
Biomarker Prioritisation and Power Estimation Using Ensemble Gene Regulatory Network Inference
Int. J. Mol. Sci. 2020, 21(21), 7886; https://doi.org/10.3390/ijms21217886 - 23 Oct 2020
Cited by 2 | Viewed by 899
Abstract
Inferring the topology of a gene regulatory network (GRN) from gene expression data is a challenging but important undertaking for gaining a better understanding of gene regulation. Key challenges include working with noisy data and dealing with a higher number of genes than [...] Read more.
Inferring the topology of a gene regulatory network (GRN) from gene expression data is a challenging but important undertaking for gaining a better understanding of gene regulation. Key challenges include working with noisy data and dealing with a higher number of genes than samples. Although a number of different methods have been proposed to infer the structure of a GRN, there are large discrepancies among the different inference algorithms they adopt, rendering their meaningful comparison challenging. In this study, we used two methods, namely the MIDER (Mutual Information Distance and Entropy Reduction) and the PLSNET (Partial least square based feature selection) methods, to infer the structure of a GRN directly from data and computationally validated our results. Both methods were applied to different gene expression datasets resulting from inflammatory bowel disease (IBD), pancreatic ductal adenocarcinoma (PDAC), and acute myeloid leukaemia (AML) studies. For each case, gene regulators were successfully identified. For example, for the case of the IBD dataset, the UGT1A family genes were identified as key regulators while upon analysing the PDAC dataset, the SULF1 and THBS2 genes were depicted. We further demonstrate that an ensemble-based approach, that combines the output of the MIDER and PLSNET algorithms, can infer the structure of a GRN from data with higher accuracy. We have also estimated the number of the samples required for potential future validation studies. Here, we presented our proposed analysis framework that caters not only to candidate regulator genes prediction for potential validation experiments but also an estimation of the number of samples required for these experiments. Full article
(This article belongs to the Special Issue OMICs, Data Integration, and Applications in Personalized Medicine)
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Article
HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene–Environment Interactions
Int. J. Mol. Sci. 2020, 21(18), 6724; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21186724 - 14 Sep 2020
Viewed by 806
Abstract
Gene–environment interaction (G×E) studies are one of the most important solutions for understanding the “missing heritability” problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this [...] Read more.
Gene–environment interaction (G×E) studies are one of the most important solutions for understanding the “missing heritability” problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene–Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene–alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data. Full article
(This article belongs to the Special Issue OMICs, Data Integration, and Applications in Personalized Medicine)
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Article
A Data-Driven Review of the Genetic Factors of Pregnancy Complications
Int. J. Mol. Sci. 2020, 21(9), 3384; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21093384 - 11 May 2020
Cited by 2 | Viewed by 2096
Abstract
Over the recent years, many advances have been made in the research of the genetic factors of pregnancy complications. In this work, we use publicly available data repositories, such as the National Human Genome Research Institute GWAS Catalog, HuGE Navigator, and the UK [...] Read more.
Over the recent years, many advances have been made in the research of the genetic factors of pregnancy complications. In this work, we use publicly available data repositories, such as the National Human Genome Research Institute GWAS Catalog, HuGE Navigator, and the UK Biobank genetic and phenotypic dataset to gain insights into molecular pathways and individual genes behind a set of pregnancy-related traits, including the most studied ones—preeclampsia, gestational diabetes, preterm birth, and placental abruption. Using both HuGE and GWAS Catalog data, we confirm that immune system and, in particular, T-cell related pathways are one of the most important drivers of pregnancy-related traits. Pathway analysis of the data reveals that cell adhesion and matrisome-related genes are also commonly involved in pregnancy pathologies. We also find a large role of metabolic factors that affect not only gestational diabetes, but also the other traits. These shared metabolic genes include IGF2, PPARG, and NOS3. We further discover that the published genetic associations are poorly replicated in the independent UK Biobank cohort. Nevertheless, we find novel genome-wide associations with pregnancy-related traits for the FBLN7, STK32B, and ACTR3B genes, and replicate the effects of the KAZN and TLE1 genes, with the latter being the only gene identified across all data resources. Overall, our analysis highlights central molecular pathways for pregnancy-related traits, and suggests a need to use more accurate and sophisticated association analysis strategies to robustly identify genetic risk factors for pregnancy complications. Full article
(This article belongs to the Special Issue OMICs, Data Integration, and Applications in Personalized Medicine)
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Review

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Review
Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases
Int. J. Mol. Sci. 2021, 22(9), 4820; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22094820 - 01 May 2021
Viewed by 839
Abstract
Molecular and clinical heterogeneity is increasingly recognized as a common characteristic of neurodegenerative diseases (NDs), such as Alzheimer’s disease, Parkinson’s disease and amyotrophic lateral sclerosis. This heterogeneity makes difficult the development of early diagnosis and effective treatment approaches, as well as the design [...] Read more.
Molecular and clinical heterogeneity is increasingly recognized as a common characteristic of neurodegenerative diseases (NDs), such as Alzheimer’s disease, Parkinson’s disease and amyotrophic lateral sclerosis. This heterogeneity makes difficult the development of early diagnosis and effective treatment approaches, as well as the design and testing of new drugs. As such, the stratification of patients into meaningful disease subgroups, with clinical and biological relevance, may improve disease management and the development of effective treatments. To this end, omics technologies—such as genomics, transcriptomics, proteomics and metabolomics—are contributing to offer a more comprehensive view of molecular pathways underlying the development of NDs, helping to differentiate subtypes of patients based on their specific molecular signatures. In this article, we discuss how omics technologies and their integration have provided new insights into the molecular heterogeneity underlying the most prevalent NDs, aiding to define early diagnosis and progression markers as well as therapeutic targets that can translate into stratified treatment approaches, bringing us closer to the goal of personalized medicine in neurology. Full article
(This article belongs to the Special Issue OMICs, Data Integration, and Applications in Personalized Medicine)
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Review
Multiomics Integration in Skin Diseases with Alterations in Notch Signaling Pathway: PlatOMICs Phase 1 Deployment
Int. J. Mol. Sci. 2021, 22(4), 1523; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms22041523 - 03 Feb 2021
Viewed by 827
Abstract
The high volume of information produced in the age of omics was and still is an important step to understanding several pathological processes, providing the enlightenment of complex molecular networks and the identification of molecular targets associated with many diseases. Despite these remarkable [...] Read more.
The high volume of information produced in the age of omics was and still is an important step to understanding several pathological processes, providing the enlightenment of complex molecular networks and the identification of molecular targets associated with many diseases. Despite these remarkable scientific advances, the majority of the results are disconnected and divergent, making their use limited. Skin diseases with alterations in the Notch signaling pathway were extensively studied during the omics era. In the GWAS Catalog, considering only studies on genomics association (GWAS), several works were deposited, some of which with divergent results. In addition, there are thousands of scientific articles available about these skin diseases. In our study, we focused our attention on skin diseases characterized by the impairment of Notch signaling, this pathway being of pivotal importance in the context of epithelial disorders. We considered the pathologies of five human skin diseases, Hidradenitis Suppurativa, Dowling Degos Disease, Adams–Oliver Syndrome, Psoriasis, and Atopic Dermatitis, in which the molecular alterations in the Notch signaling pathway have been reported. To this end, we started developing a new multiomics platform, PlatOMICs, to integrate and re-analyze omics information, searching for the molecular interactions involved in the pathogenesis of skin diseases with alterations in the Notch signaling pathway. Full article
(This article belongs to the Special Issue OMICs, Data Integration, and Applications in Personalized Medicine)
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