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Biomedicines
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Bioinformatics and Its Application in Biomedicine
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Bioinformatics and Its Application in Biomedicine

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 46332

Special Issue Editor

Dr. Thomas Mohr

E-Mail Website
Guest Editor
1. Department of Medicine I, Institute of Cancer Research and Comprehensive Cancer Center, Medical University Vienna, Vienna, Austria
2. ScienceConsult - DI Thomas Mohr KG, Enzianweg 10a, 2353 Guntramsdorf, Austria
Interests: bioinformatics; systems biology; cancer; biomarker identification; network analysis; omics analysis; in-vitro test development; data integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bioinformatics has become an indispensable tool in molecular biology. Analytic tools provided by bioinformaticians have allowed unprecedented insights into many aspects of medicine. For example, the generation and analysis of omics data in the context of biologic functions and pathways has permitted revolutionary advancements in terms of disease classification and the identification of therapeutic targets and biomarkers.

This Special Issue aims at describing state-of-the-art methods in bioinformatics and their application in biomedicine. The main topics covered will be:

  • The analysis of genomics, transcriptomics, proteomics and metabolomics data;
  • The determination of the biologic contexts of omics data;
  • The integration of omics data;
  • The analysis of gene–gene interaction networks;
  • In silico dissection;
  • The determination of biomarkers using network-based methods and AI;
  • What are the current trends in bioinformatics?

The focus will be on papers linking bioinformatics and the wet lab; therefore, they should ideally include confirmatory experiments. However, papers will be considered case by case based on overall merit.

Dr. Thomas Mohr
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. Biomedicines 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
  • proteomics
  • metabolomics
  • biologic context of omics data
  • integration of omics techniques
  • co-expression network analysis
  • gene–gene interaction networks
  • artificial intelligence in molecular biology

 

Published Papers (15 papers)

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Research

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21 pages, 5797 KiB  
Article
Concerted Regulation of Glycosylation Factors Sustains Tissue Identity and Function
by Daniel Sobral, Rita Francisco, Laura Duro, Paula Alexandra Videira and Ana Rita Grosso
Biomedicines 2022, 10(8), 1805; https://doi.org/10.3390/biomedicines10081805 - 27 Jul 2022
Cited by 2 | Viewed by 2035
Abstract
Glycosylation is a fundamental cellular process affecting human development and health. Complex machinery establishes the glycan structures whose heterogeneity provides greater structural diversity than other post-translational modifications. Although known to present spatial and temporal diversity, the evolution of glycosylation and its role at [...] Read more.
Glycosylation is a fundamental cellular process affecting human development and health. Complex machinery establishes the glycan structures whose heterogeneity provides greater structural diversity than other post-translational modifications. Although known to present spatial and temporal diversity, the evolution of glycosylation and its role at the tissue-specific level is poorly understood. In this study, we combined genome and transcriptome profiles of healthy and diseased tissues to uncover novel insights into the complex role of glycosylation in humans. We constructed a catalogue of human glycosylation factors, including transferases, hydrolases and other genes directly involved in glycosylation. These were categorized as involved in N-, O- and lipid-linked glycosylation, glypiation, and glycosaminoglycan synthesis. Our data showed that these glycosylation factors constitute an ancient family of genes, where evolutionary constraints suppressed large gene duplications, except for genes involved in O-linked and lipid glycosylation. The transcriptome profiles of 30 healthy human tissues revealed tissue-specific expression patterns preserved across mammals. In addition, clusters of tightly co-expressed genes suggest a glycosylation code underlying tissue identity. Interestingly, several glycosylation factors showed tissue-specific profiles varying with age, suggesting a role in ageing-related disorders. In cancer, our analysis revealed that glycosylation factors are highly perturbed, at the genome and transcriptome levels, with a strong predominance of copy number alterations. Moreover, glycosylation factor dysregulation was associated with distinct cellular compositions of the tumor microenvironment, reinforcing the impact of glycosylation in modulating the immune system. Overall, this work provides genome-wide evidence that the glycosylation machinery is tightly regulated in healthy tissues and impaired in ageing and tumorigenesis, unveiling novel potential roles as prognostic biomarkers or therapeutic targets. Full article
(This article belongs to the Special Issue Bioinformatics and Its Application in Biomedicine)
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17 pages, 2654 KiB  
Article
Network Proximity-Based Drug Repurposing Strategy for Early and Late Stages of Primary Biliary Cholangitis
by Endrit Shahini, Giuseppe Pasculli, Andrea Mastropietro, Paola Stolfi, Paolo Tieri, Davide Vergni, Raffaele Cozzolongo, Francesco Pesce and Gianluigi Giannelli
Biomedicines 2022, 10(7), 1694; https://doi.org/10.3390/biomedicines10071694 - 13 Jul 2022
Cited by 3 | Viewed by 2340
Abstract
Primary biliary cholangitis (PBC) is a chronic, cholestatic, immune-mediated, and progressive liver disorder. Treatment to preventing the disease from advancing into later and irreversible stages is still an unmet clinical need. Accordingly, we set up a drug repurposing framework to find potential therapeutic [...] Read more.
Primary biliary cholangitis (PBC) is a chronic, cholestatic, immune-mediated, and progressive liver disorder. Treatment to preventing the disease from advancing into later and irreversible stages is still an unmet clinical need. Accordingly, we set up a drug repurposing framework to find potential therapeutic agents targeting relevant pathways derived from an expanded pool of genes involved in different stages of PBC. Starting with updated human protein–protein interaction data and genes specifically involved in the early and late stages of PBC, a network medicine approach was used to provide a PBC “proximity” or “involvement” gene ranking using network diffusion algorithms and machine learning models. The top genes in the proximity ranking, when combined with the original PBC-related genes, resulted in a final dataset of the genes most involved in PBC disease. Finally, a drug repurposing strategy was implemented by mining and utilizing dedicated drug–gene interaction and druggable genome information knowledge bases (e.g., the DrugBank repository). We identified several potential drug candidates interacting with PBC pathways after performing an over-representation analysis on our initial 1121-seed gene list and the resulting disease-associated (algorithm-obtained) genes. The mechanism and potential therapeutic applications of such drugs were then thoroughly discussed, with a particular emphasis on different stages of PBC disease. We found that interleukin/EGFR/TNF-alpha inhibitors, branched-chain amino acids, geldanamycin, tauroursodeoxycholic acid, genistein, antioestrogens, curcumin, antineovascularisation agents, enzyme/protease inhibitors, and antirheumatic agents are promising drugs targeting distinct stages of PBC. We developed robust and transparent selection mechanisms for prioritizing already approved medicinal products or investigational products for repurposing based on recognized unmet medical needs in PBC, as well as solid preliminary data to achieve this goal. Full article
(This article belongs to the Special Issue Bioinformatics and Its Application in Biomedicine)
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14 pages, 1853 KiB  
Article
Predicting High Blood Pressure Using DNA Methylome-Based Machine Learning Models
by Thi Mai Nguyen, Hoang Long Le, Kyu-Baek Hwang, Yun-Chul Hong and Jin Hee Kim
Biomedicines 2022, 10(6), 1406; https://doi.org/10.3390/biomedicines10061406 - 14 Jun 2022
Cited by 3 | Viewed by 2164
Abstract
DNA methylation modification plays a vital role in the pathophysiology of high blood pressure (BP). Herein, we applied three machine learning (ML) algorithms including deep learning (DL), support vector machine, and random forest for detecting high BP using DNA methylome data. Peripheral blood [...] Read more.
DNA methylation modification plays a vital role in the pathophysiology of high blood pressure (BP). Herein, we applied three machine learning (ML) algorithms including deep learning (DL), support vector machine, and random forest for detecting high BP using DNA methylome data. Peripheral blood samples of 50 elderly individuals were collected three times at three visits for DNA methylome profiling. Participants who had a history of hypertension and/or current high BP measure were considered to have high BP. The whole dataset was randomly divided to conduct a nested five-group cross-validation for prediction performance. Data in each outer training set were independently normalized using a min–max scaler, reduced dimensionality using principal component analysis, then fed into three predictive algorithms. Of the three ML algorithms, DL achieved the best performance (AUPRC = 0.65, AUROC = 0.73, accuracy = 0.69, and F1-score = 0.73). To confirm the reliability of using DNA methylome as a biomarker for high BP, we constructed mixed-effects models and found that 61,694 methylation sites located in 15,523 intragenic regions and 16,754 intergenic regions were significantly associated with BP measures. Our proposed models pioneered the methodology of applying ML and DNA methylome data for early detection of high BP in clinical practices. Full article
(This article belongs to the Special Issue Bioinformatics and Its Application in Biomedicine)
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13 pages, 2268 KiB  
Article
Corpuscular Fragility and Metabolic Aspects of Freshly Drawn Beta-Thalassemia Minor RBCs Impact Their Physiology and Performance Post Transfusion: A Triangular Correlation Analysis In Vitro and In Vivo
by Alkmini T. Anastasiadi, Vasiliki-Zoi Arvaniti, Efthymios C. Paronis, Nikolaos G. Kostomitsopoulos, Konstantinos Stamoulis, Issidora S. Papassideri, Angelo D’Alessandro, Anastasios G. Kriebardis, Vassilis L. Tzounakas and Marianna H. Antonelou
Biomedicines 2022, 10(3), 530; https://doi.org/10.3390/biomedicines10030530 - 23 Feb 2022
Cited by 4 | Viewed by 2032
Abstract
The clarification of donor variation effects upon red blood cell (RBC) storage lesion and transfusion efficacy may open new ways for donor–recipient matching optimization. We hereby propose a “triangular” strategy for studying the links comprising the transfusion chain—donor, blood product, recipient—as exemplified in [...] Read more.
The clarification of donor variation effects upon red blood cell (RBC) storage lesion and transfusion efficacy may open new ways for donor–recipient matching optimization. We hereby propose a “triangular” strategy for studying the links comprising the transfusion chain—donor, blood product, recipient—as exemplified in two cohorts of control and beta-thalassemia minor (βThal+) donors (n = 18 each). It was unraveled that RBC osmotic fragility and caspase-like proteasomal activity can link both donor cohorts to post-storage states. In the case of heterozygotes, the geometry, size and intrinsic low RBC fragility might be lying behind their higher post-storage resistance to lysis and recovery in mice. Moreover, energy-related molecules (e.g., phosphocreatine) and purine metabolism factors (IMP, hypoxanthine) were specifically linked to lower post-storage hemolysis and phosphatidylserine exposure. The latter was also ameliorated by antioxidants, such as urate. Finally, higher proteasomal conservation across the transfusion chain was observed in heterozygotes compared to control donors. The proposed “triangularity model” can be (a) expanded to additional donor/recipient backgrounds, (b) enriched by big data, especially in the post-transfusion state and (c) fuel targeted experiments in order to discover new quality biomarkers and design more personalized transfusion medicine schemes. Full article
(This article belongs to the Special Issue Bioinformatics and Its Application in Biomedicine)
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19 pages, 6321 KiB  
Article
Cellular Phenotypic Transformation in Heart Failure Caused by Coronary Heart Disease and Dilated Cardiomyopathy: Delineating at Single-Cell Level
by Luojiang Zhu, Wen Wang, Changzhen Ren, Yangkai Wang, Guanghao Zhang, Jianmin Liu and Weizhong Wang
Biomedicines 2022, 10(2), 402; https://doi.org/10.3390/biomedicines10020402 - 08 Feb 2022
Cited by 2 | Viewed by 1988
Abstract
Heart failure (HF) is known as the final manifestation of cardiovascular diseases. Although cellular heterogeneity of the heart is well understood, the phenotypic transformation of cardiac cells in progress of HF remains obscure. This study aimed to analyze phenotypic transformation of cardiac cells [...] Read more.
Heart failure (HF) is known as the final manifestation of cardiovascular diseases. Although cellular heterogeneity of the heart is well understood, the phenotypic transformation of cardiac cells in progress of HF remains obscure. This study aimed to analyze phenotypic transformation of cardiac cells in HF through human single-cell RNA transcriptome profile. Here, phenotypic transformation of cardiomyocytes (CMs), endothelial cells (ECs), and fibroblasts was identified by data analysis and animal experiments. Abnormal myosin subunits including the decrease in Myosin Heavy Chain 6, Myosin Light Chain 7 and the increase in Myosin Heavy Chain 7 were found in CMs. Two disease phenotypes of ECs named inflammatory ECs and muscularized ECs were identified. In addition, myofibroblast was increased in HF and highly associated with abnormal extracellular matrix. Our study proposed an integrated map of phenotypic transformation of cardiac cells and highlighted the intercellular communication in HF. This detailed definition of cellular transformation will facilitate cell-based mapping of novel interventional targets for the treatment of HF. Full article
(This article belongs to the Special Issue Bioinformatics and Its Application in Biomedicine)
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23 pages, 3358 KiB  
Article
Dissecting the Transcriptomes of Multiple Metronidazole-Resistant and Sensitive Trichomonas vaginalis Strains Identified Distinct Genes and Pathways Associated with Drug Resistance and Cell Death
by Po-Jung Huang, Ching-Yun Huang, Yu-Xuan Li, Yi-Chung Liu, Lichieh-Julie Chu, Yuan-Ming Yeh, Wei-Hung Cheng, Ruei-Ming Chen, Chi-Ching Lee, Lih-Chyang Chen, Hsin-Chung Lin, Shu-Fang Chiu, Wei-Ning Lin, Ping-Chiang Lyu, Petrus Tang and Kuo-Yang Huang
Biomedicines 2021, 9(12), 1817; https://doi.org/10.3390/biomedicines9121817 - 02 Dec 2021
Cited by 6 | Viewed by 2286
Abstract
Trichomonas vaginalis is the causative agent of trichomoniasis, the most prevalent non-viral sexually transmitted infection worldwide. Metronidazole (MTZ) is the mainstay of anti-trichomonal chemotherapy; however, drug resistance has become an increasingly worrying issue. Additionally, the molecular events of MTZ-induced cell death in T. [...] Read more.
Trichomonas vaginalis is the causative agent of trichomoniasis, the most prevalent non-viral sexually transmitted infection worldwide. Metronidazole (MTZ) is the mainstay of anti-trichomonal chemotherapy; however, drug resistance has become an increasingly worrying issue. Additionally, the molecular events of MTZ-induced cell death in T. vaginalis remain elusive. To gain insight into the differential expression of genes related to MTZ resistance and cell death, we conducted RNA-sequencing of three paired MTZ-resistant (MTZ-R) and MTZ-sensitive (MTZ-S) T. vaginalis strains treated with or without MTZ. Comparative transcriptomes analysis identified that several putative drug-resistant genes were exclusively upregulated in different MTZ-R strains, such as ATP-binding cassette (ABC) transporters and multidrug resistance pumps. Additionally, several shared upregulated genes among all the MTZ-R transcriptomes were not previously identified in T. vaginalis, such as 5′-nucleotidase surE and Na+-driven multidrug efflux pump, which are a potential stress response protein and a multidrug and toxic compound extrusion (MATE)-like protein, respectively. Functional enrichment analysis revealed that purine and pyrimidine metabolisms were suppressed in MTZ-S parasites upon drug treatment, whereas the endoplasmic reticulum-associated degradation (ERAD) pathway, proteasome, and ubiquitin-mediated proteolysis were strikingly activated, highlighting the novel pathways responsible for drug-induced stress. Our work presents the most detailed analysis of the transcriptional changes and the regulatory networks associated with MTZ resistance and MTZ-induced signaling, providing insights into MTZ resistance and cell death mechanisms in trichomonads. Full article
(This article belongs to the Special Issue Bioinformatics and Its Application in Biomedicine)
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25 pages, 3133 KiB  
Article
Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles
by Linh C. Nguyen, Stefan Naulaerts, Alejandra Bruna, Ghita Ghislat and Pedro J. Ballester
Biomedicines 2021, 9(10), 1319; https://doi.org/10.3390/biomedicines9101319 - 26 Sep 2021
Cited by 13 | Viewed by 3300
Abstract
(1) Background: Inter-tumour heterogeneity is one of cancer’s most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, single-gene markers of response are rare and/or may fail to achieve a significant impact in the clinic. [...] Read more.
(1) Background: Inter-tumour heterogeneity is one of cancer’s most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, single-gene markers of response are rare and/or may fail to achieve a significant impact in the clinic. Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. (2) Methods: Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. (3) Results: Combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: paclitaxel (breast cancer), binimetinib (breast cancer) and cetuximab (colorectal cancer). Interestingly, each of these multi-gene ML models identifies some treatment-responsive PDXs not harbouring the best actionable mutation for that case. Thus, ML multi-gene predictors generally have much fewer false negatives than the corresponding single-gene marker. (4) Conclusions: As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if ML algorithms were also applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations. Full article
(This article belongs to the Special Issue Bioinformatics and Its Application in Biomedicine)
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12 pages, 1943 KiB  
Article
OmniSARS2: A Highly Sensitive and Specific RT-qPCR-Based COVID-19 Diagnostic Method Designed to Withstand SARS-CoV-2 Lineage Evolution
by Eduarda Carvalho-Correia, Carla Calçada, Fernando Branca, Nuria Estévez-Gómez, Loretta De Chiara, Nair Varela, Pilar Gallego-García, David Posada, Hugo Sousa, João Sousa, Maria Isabel Veiga and Nuno S. Osório
Biomedicines 2021, 9(10), 1314; https://doi.org/10.3390/biomedicines9101314 - 26 Sep 2021
Cited by 8 | Viewed by 3178
Abstract
Extensive transmission of SARS-CoV-2 during the COVID-19 pandemic allowed the generation of thousands of mutations within its genome. While several of these become rare, others largely increase in prevalence, potentially jeopardizing the sensitivity of PCR-based diagnostics. Taking advantage of SARS-CoV-2 genomic knowledge, we [...] Read more.
Extensive transmission of SARS-CoV-2 during the COVID-19 pandemic allowed the generation of thousands of mutations within its genome. While several of these become rare, others largely increase in prevalence, potentially jeopardizing the sensitivity of PCR-based diagnostics. Taking advantage of SARS-CoV-2 genomic knowledge, we designed a one-step probe-based multiplex RT-qPCR (OmniSARS2) to simultaneously detect short fragments of the SARS-CoV-2 genome in ORF1ab, E gene and S gene. Comparative genomics of the most common SARS-CoV-2 lineages, other human betacoronavirus and alphacoronavirus, was the basis for this design, targeting both highly conserved regions across SARS-CoV-2 lineages and variable or absent in other Coronaviridae viruses. The highest analytical sensitivity of this method for SARS-CoV-2 detection was 94.2 copies/mL at 95% detection probability (~1 copy per total reaction volume) for the S gene assay, matching the most sensitive available methods. In vitro specificity tests, performed using reference strains, showed no cross-reactivity with other human coronavirus or common pathogens. The method was compared with commercially available methods and detected the virus in clinical samples encompassing different SARS-CoV-2 lineages, including B.1, B.1.1, B.1.177 or B.1.1.7 and rarer lineages. OmniSARS2 revealed a sensitive and specific viral detection method that is less likely to be affected by lineage evolution oligonucleotide–sample mismatch, of relevance to ensure the accuracy of COVID-19 molecular diagnostic methods. Full article
(This article belongs to the Special Issue Bioinformatics and Its Application in Biomedicine)
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19 pages, 5185 KiB  
Article
Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis
by Huy-Hoang Dang, Hoang Dang Khoa Ta, Truc T. T. Nguyen, Gangga Anuraga, Chih-Yang Wang, Kuen-Haur Lee and Nguyen Quoc Khanh Le
Biomedicines 2021, 9(9), 1144; https://doi.org/10.3390/biomedicines9091144 - 03 Sep 2021
Cited by 20 | Viewed by 3417
Abstract
G-protein signaling modulators (GPSMs) are a class of proteins involved in the regulation of G protein-coupled receptors, the most abundant family of cell-surface receptors that are crucial in the development of various tumors, including breast cancer. This study aims to identify the potential [...] Read more.
G-protein signaling modulators (GPSMs) are a class of proteins involved in the regulation of G protein-coupled receptors, the most abundant family of cell-surface receptors that are crucial in the development of various tumors, including breast cancer. This study aims to identify the potential therapeutic and prognostic roles of GPSMs in breast cancer. Oncomine and UALCAN databases were queried to determine GPSM expression levels in breast cancer tissues compared to normal samples. Survival analysis was conducted to reveal the prognostic significance of GPSMs in individuals with breast cancer. Functional enrichment analysis was performed using cBioPortal and MetaCore platforms. Finally, the association between GPSMs and immune infiltration cells in breast cancer was identified using the TIMER server. The experimental results then showed that all GPSM family members were significantly differentially expressed in breast cancer according to Oncomine and UALCAN data. Their expression levels were also associated with advanced tumor stages, and GPSM2 was found to be related to worse distant metastasis-free survival in patients with breast cancer. Functional enrichment analysis indicated that GPSMs were largely involved in cell division and cell cycle pathways. Finally, GPSM3 expression was correlated with the infiltration of several immune cells. Members of the GPSM class were differentially expressed in breast cancer. In conclusion, expression of GPSM2 was linked with worse distant metastasis-free outcomes, and hence could potentially serve as a prognostic biomarker. Furthermore, GPSM3 has potential to be a possible target for immunotherapy for breast cancer. Full article
(This article belongs to the Special Issue Bioinformatics and Its Application in Biomedicine)
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25 pages, 5090 KiB  
Article
Neural Precursor Cells Expanded Inside the 3D Micro-Scaffold Nichoid Present Different Non-Coding RNAs Profiles and Transcript Isoforms Expression: Possible Epigenetic Modulation by 3D Growth
by Letizia Messa, Bianca Barzaghini, Federica Rey, Cecilia Pandini, Gian Vincenzo Zuccotti, Cristina Cereda, Stephana Carelli and Manuela Teresa Raimondi
Biomedicines 2021, 9(9), 1120; https://doi.org/10.3390/biomedicines9091120 - 31 Aug 2021
Cited by 5 | Viewed by 2016
Abstract
Non-coding RNAs show relevant implications in various biological and pathological processes. Thus, understanding the biological implications of these molecules in stem cell biology still represents a major challenge. The aim of this work is to study the transcriptional dysregulation of 357 non-coding genes, [...] Read more.
Non-coding RNAs show relevant implications in various biological and pathological processes. Thus, understanding the biological implications of these molecules in stem cell biology still represents a major challenge. The aim of this work is to study the transcriptional dysregulation of 357 non-coding genes, found through RNA-Seq approach, in murine neural precursor cells expanded inside the 3D micro-scaffold Nichoid versus standard culture conditions. Through weighted co-expression network analysis and functional enrichment, we highlight the role of non-coding RNAs in altering the expression of coding genes involved in mechanotransduction, stemness, and neural differentiation. Moreover, as non-coding RNAs are poorly conserved between species, we focus on those with human homologue sequences, performing further computational characterization. Lastly, we looked for isoform switching as possible mechanism in altering coding and non-coding gene expression. Our results provide a comprehensive dissection of the 3D scaffold Nichoid’s influence on the biological and genetic response of neural precursor cells. These findings shed light on the possible role of non-coding RNAs in 3D cell growth, indicating that also non-coding RNAs are implicated in cellular response to mechanical stimuli. Full article
(This article belongs to the Special Issue Bioinformatics and Its Application in Biomedicine)
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16 pages, 6030 KiB  
Article
Gene Module Analysis Reveals Cell-Type Specificity and Potential Target Genes in Autism’s Pathogenesis
by Guoli Ji, Shuchao Li, Lishan Ye and Jinting Guan
Biomedicines 2021, 9(4), 410; https://doi.org/10.3390/biomedicines9040410 - 10 Apr 2021
Cited by 4 | Viewed by 2944
Abstract
Multiple genetic factors contribute to the pathogenesis of autism spectrum disorder (ASD), a kind of neurodevelopmental disorder. Genes were usually studied separately for their associations with ASD. However, genes associated with ASD do not act alone but interact with each other in a [...] Read more.
Multiple genetic factors contribute to the pathogenesis of autism spectrum disorder (ASD), a kind of neurodevelopmental disorder. Genes were usually studied separately for their associations with ASD. However, genes associated with ASD do not act alone but interact with each other in a network module. The identification of these modules is the basis for the systematic understanding of the pathogenesis of ASD. Moreover, ASD is characterized by highly pathogenic heterogeneity, and gene modules associated with ASD are cell-type-specific. In this study, based on the single-nucleus RNA sequencing data of 41 post-mortem tissue samples from the prefrontal cortex and anterior cingulate cortex of 19 ASD patients and 16 control individuals, we applied sparse module activity factorization, a matrix decomposition method consistent with the multi-factor and heterogeneous characteristics of ASD pathogenesis, to identify cell-type-specific gene modules. Then, statistical procedures were performed to detect highly reproducible cell-type-specific ASD-associated gene modules. Through the enrichment analysis of cell markers, 31 cell-type-specific gene modules related to ASD were further screened out. These 31 gene modules are all enriched with curated ASD risk genes. Finally, we utilized the expression patterns of these cell-type-specific ASD-associated gene modules to build predictive models for ASD. The excellent predictive performance also proved the associations between these gene modules and ASD. Our study confirmed the multifactorial and cell-type-specific characteristics of ASD pathogeneses. The results showed that excitatory neurons such as L2/3, L4, and L5/6-CC play essential roles in ASD’s pathogenic processes. We identified the potential ASD target genes that act together in cell-type-specific modules, such as NRG3, KCNIP4, BAI3, PTPRD, LRRTM4, and LINGO2 in the L2/3 gene modules. Our study offers new potential genomic targets for ASD and provides a novel method to study gene modules involved in the pathogenesis of ASD. Full article
(This article belongs to the Special Issue Bioinformatics and Its Application in Biomedicine)
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Review

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20 pages, 764 KiB  
Review
Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice
by Serena Dotolo, Riziero Esposito Abate, Cristin Roma, Davide Guido, Alessia Preziosi, Beatrice Tropea, Fernando Palluzzi, Luciano Giacò and Nicola Normanno
Biomedicines 2022, 10(9), 2074; https://doi.org/10.3390/biomedicines10092074 - 24 Aug 2022
Cited by 10 | Viewed by 3704
Abstract
The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic [...] Read more.
The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient’s tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD). Full article
(This article belongs to the Special Issue Bioinformatics and Its Application in Biomedicine)
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30 pages, 5159 KiB  
Review
Applications of Neural Networks in Biomedical Data Analysis
by Romano Weiss, Sanaz Karimijafarbigloo, Dirk Roggenbuck and Stefan Rödiger
Biomedicines 2022, 10(7), 1469; https://doi.org/10.3390/biomedicines10071469 - 21 Jun 2022
Cited by 7 | Viewed by 2624
Abstract
Neural networks for deep-learning applications, also called artificial neural networks, are important tools in science and industry. While their widespread use was limited because of inadequate hardware in the past, their popularity increased dramatically starting in the early 2000s when it became possible [...] Read more.
Neural networks for deep-learning applications, also called artificial neural networks, are important tools in science and industry. While their widespread use was limited because of inadequate hardware in the past, their popularity increased dramatically starting in the early 2000s when it became possible to train increasingly large and complex networks. Today, deep learning is widely used in biomedicine from image analysis to diagnostics. This also includes special topics, such as forensics. In this review, we discuss the latest networks and how they work, with a focus on the analysis of biomedical data, particularly biomarkers in bioimage data. We provide a summary on numerous technical aspects, such as activation functions and frameworks. We also present a data analysis of publications about neural networks to provide a quantitative insight into the use of network types and the number of journals per year to determine the usage in different scientific fields. Full article
(This article belongs to the Special Issue Bioinformatics and Its Application in Biomedicine)
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31 pages, 4263 KiB  
Review
A Bioinformatics-Assisted Review on Iron Metabolism and Immune System to Identify Potential Biomarkers of Exercise Stress-Induced Immunosuppression
by Diego A. Bonilla, Yurany Moreno, Jorge L. Petro, Diego A. Forero, Salvador Vargas-Molina, Adrián Odriozola-Martínez, Carlos A. Orozco, Jeffrey R. Stout, Eric S. Rawson and Richard B. Kreider
Biomedicines 2022, 10(3), 724; https://doi.org/10.3390/biomedicines10030724 - 21 Mar 2022
Cited by 10 | Viewed by 5179
Abstract
The immune function is closely related to iron (Fe) homeostasis and allostasis. The aim of this bioinformatics-assisted review was twofold; (i) to update the current knowledge of Fe metabolism and its relationship to the immune system, and (ii) to perform a prediction analysis [...] Read more.
The immune function is closely related to iron (Fe) homeostasis and allostasis. The aim of this bioinformatics-assisted review was twofold; (i) to update the current knowledge of Fe metabolism and its relationship to the immune system, and (ii) to perform a prediction analysis of regulatory network hubs that might serve as potential biomarkers during stress-induced immunosuppression. Several literature and bioinformatics databases/repositories were utilized to review Fe metabolism and complement the molecular description of prioritized proteins. The Search Tool for the Retrieval of Interacting Genes (STRING) was used to build a protein-protein interactions network for subsequent network topology analysis. Importantly, Fe is a sensitive double-edged sword where two extremes of its nutritional status may have harmful effects on innate and adaptive immunity. We identified clearly connected important hubs that belong to two clusters: (i) presentation of peptide antigens to the immune system with the involvement of redox reactions of Fe, heme, and Fe trafficking/transport; and (ii) ubiquitination, endocytosis, and degradation processes of proteins related to Fe metabolism in immune cells (e.g., macrophages). The identified potential biomarkers were in agreement with the current experimental evidence, are included in several immunological/biomarkers databases, and/or are emerging genetic markers for different stressful conditions. Although further validation is warranted, this hybrid method (human-machine collaboration) to extract meaningful biological applications using available data in literature and bioinformatics tools should be highlighted. Full article
(This article belongs to the Special Issue Bioinformatics and Its Application in Biomedicine)
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31 pages, 1440 KiB  
Review
Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data
by Thi Mai Nguyen, Nackhyoung Kim, Da Hae Kim, Hoang Long Le, Md Jalil Piran, Soo-Jong Um and Jin Hee Kim
Biomedicines 2021, 9(11), 1733; https://doi.org/10.3390/biomedicines9111733 - 20 Nov 2021
Cited by 8 | Viewed by 4135
Abstract
Deep learning (DL) is a distinct class of machine learning that has achieved first-class performance in many fields of study. For epigenomics, the application of DL to assist physicians and scientists in human disease-relevant prediction tasks has been relatively unexplored until very recently. [...] Read more.
Deep learning (DL) is a distinct class of machine learning that has achieved first-class performance in many fields of study. For epigenomics, the application of DL to assist physicians and scientists in human disease-relevant prediction tasks has been relatively unexplored until very recently. In this article, we critically review published studies that employed DL models to predict disease detection, subtype classification, and treatment responses, using epigenomic data. A comprehensive search on PubMed, Scopus, Web of Science, Google Scholar, and arXiv.org was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Among 1140 initially identified publications, we included 22 articles in our review. DNA methylation and RNA-sequencing data are most frequently used to train the predictive models. The reviewed models achieved a high accuracy ranged from 88.3% to 100.0% for disease detection tasks, from 69.5% to 97.8% for subtype classification tasks, and from 80.0% to 93.0% for treatment response prediction tasks. We generated a workflow to develop a predictive model that encompasses all steps from first defining human disease-related tasks to finally evaluating model performance. DL holds promise for transforming epigenomic big data into valuable knowledge that will enhance the development of translational epigenomics. Full article
(This article belongs to the Special Issue Bioinformatics and Its Application in Biomedicine)
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