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A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Biochemistry, Biophysics and Computational Biology".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 25549

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Dr. Qian Du

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Guest Editor

Department of Biological Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA
Interests: bioinformatics; statistical genetics; data analysis; omics; cancers; cardiovascular diseases; obesity; neurodegenerative diseases; mental illness

Special Issue Information

Dear Colleagues,

In the past decade, an unprecedented wealth of omics data has been produced by next-generation sequencing technologies and advanced Artificial Intelligence methods. The characterization and quantification of human diseases at the molecular level has never been so detailed. Omics data, such as epigenomics, metabolomics, transcriptomics, and genomics, bring a remarkable opportunity for scientists to understand the disease mechanism in a comprehensive way. The challenges coming along with the data explosion are data management, data mining, data integration, data analysis, and data interpretation.

Bioinformatics is an interdisciplinary subject of biology and computer science dealing with the above challenges. Numerous bioinformatics tools have been developed to promote the integration of omics data with traditional clinical data. The application of bioinformatics in the medical field has deepened the functional understanding of human disease mechanisms, improved the accuracy of human disease diagnosis and prognosis, and led to the discovery of novel drug targets. The contribution of bioinformatics to the medical field makes it possible to provide tailored treatment to each patient instead of treating them as an average, which is the core value of precision medicine.

The goal of this Special Issue of Life is for omics data to be manipulated appropriately, and for physicians and researchers to have a rule of thumb to follow. We encourage contributions to the development and applications of bioinformatics and statistical methods in the context of human diseases. Original studies, as well as insightful reviews, are very welcome to be published in this Special Issue.

Dr. Qian Du
Guest Editor

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Keywords

bioinformatics
statistical genetics
data analysis
omics
cancers, cardiovascular diseases, obesity, neurodegenerative diseases, mental illness

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Published Papers (9 papers)

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Research

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12 pages, 2112 KiB

Open AccessArticle

Radiomics Analysis of Preprocedural CT Imaging for Outcome Prediction after Transjugular Intrahepatic Portosystemic Shunt Creation

by Giuseppe Mamone, Albert Comelli, Giorgia Porrello, Mariapina Milazzo, Ambra Di Piazza, Alessandro Stefano, Viviana Benfante, Antonino Tuttolomondo, Gianvincenzo Sparacia, Luigi Maruzzelli and Roberto Miraglia

Life 2024, 14(6), 726; https://doi.org/10.3390/life14060726 - 3 Jun 2024

Cited by 1 | Viewed by 825

Abstract

Purpose: To evaluate the role of radiomics in preoperative outcome prediction in cirrhotic patients who underwent transjugular intrahepatic portosystemic shunt (TIPS) using “controlled expansion covered stents”. Materials and Methods: This retrospective institutional review board-approved study included cirrhotic patients undergoing TIPS with controlled expansion covered stent placement. From preoperative CT images, the whole liver was segmented into Volumes of Interest (VOIs) at the unenhanced and portal venous phase. Radiomics features were extracted, collected, and analyzed. Subsequently, receiver operating characteristic (ROC) curves were drawn to assess which features could predict patients’ outcomes. The endpoints studied were 6-month overall survival (OS), development of hepatic encephalopathy (HE), grade II or higher HE according to West Haven Criteria, and clinical response, defined as the absence of rebleeding or ascites. A radiomic model for outcome prediction was then designed. Results: A total of 76 consecutive cirrhotic patients undergoing TIPS creation were enrolled. The highest performances in terms of the area under the receiver operating characteristic curve (AUROC) were observed for the “clinical response” and “survival at 6 months” outcome with 0.755 and 0.767, at the unenhanced and portal venous phase, respectively. Specifically, on basal scans, accuracy, specificity, and sensitivity were 66.42%, 63.93%, and 73.75%, respectively. At the portal venous phase, an accuracy of 65.34%, a specificity of 62.38%, and a sensitivity of 74.00% were demonstrated. Conclusions: A pre-interventional machine learning-based CT radiomics algorithm could be useful in predicting survival and clinical response after TIPS creation in cirrhotic patients. Full article

(This article belongs to the Special Issue Application Research of Bioinformatics in Human Diseases)

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10 pages, 1162 KiB

Open AccessArticle

Mathematical Modeling of Non-Small-Cell Lung Cancer Biology through the Experimental Data on Cell Composition and Growth of Patient-Derived Organoids

by Rushan Sulimanov, Konstantin Koshelev, Vladimir Makarov, Alexandre Mezentsev, Mikhail Durymanov, Lilian Ismail, Komal Zahid, Yegor Rumyantsev and Ilya Laskov

Life 2023, 13(11), 2228; https://doi.org/10.3390/life13112228 - 20 Nov 2023

Cited by 3 | Viewed by 2090

Abstract

Mathematical models of non-small-cell lung cancer are powerful tools that use clinical and experimental data to describe various aspects of tumorigenesis. The developed algorithms capture phenotypic changes in the tumor and predict changes in tumor behavior, drug resistance, and clinical outcomes of anti-cancer therapy. The aim of this study was to propose a mathematical model that predicts the changes in the cellular composition of patient-derived tumor organoids over time with a perspective of translation of these results to the parental tumor, and therefore to possible clinical course and outcomes for the patient. Using the data on specific biomarkers of cancer cells (PD-L1), tumor-associated macrophages (CD206), natural killer cells (CD8), and fibroblasts (αSMA) as input, we proposed a model that accurately predicts the cellular composition of patient-derived tumor organoids at a desired time point. Combining the obtained results with “omics” approaches will improve our understanding of the nature of non-small-cell lung cancer. Moreover, their implementation into clinical practice will facilitate a decision-making process on treatment strategy and develop a new personalized approach in anti-cancer therapy. Full article

(This article belongs to the Special Issue Application Research of Bioinformatics in Human Diseases)

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16 pages, 2892 KiB

Open AccessArticle

Prediction of Amino Acid Substitutions in ABL1 Protein Leading to Tumor Drug Resistance Based on “Structure-Property” Relationship Classification Models

by Svetlana I. Zhuravleva, Anton D. Zadorozhny, Boris V. Shilov and Alexey A. Lagunin

Life 2023, 13(9), 1807; https://doi.org/10.3390/life13091807 - 24 Aug 2023

Cited by 1 | Viewed by 1480

Abstract

Drug resistance to anticancer drugs is a serious complication in patients with cancer. Typically, drug resistance occurs due to amino acid substitutions (AAS) in drug target proteins. The study aimed at developing and validating a new approach to the creation of structure-property relationships (SPR) classification models to predict AASs leading to drug resistance to inhibitors of tyrosine-protein kinase ABL1. The approach was based on the representation of AASs as peptides described in terms of structural formulas. The data on drug-resistant and non-resistant variants of AAS for two isoforms of ABL1 were extracted from the COSMIC database. The given training sets (approximately 700 missense variants) were used for the creation of SPR models in MultiPASS software based on substructural atom-centric multiple neighborhoods of atom (MNA) descriptors for the description of the structural formula of protein fragments and a Bayesian-like algorithm for revealing structure-property relationships. It was found that MNA descriptors of the 6th level and peptides from 11 amino acid residues were the best combination for ABL1 isoform 1 with the prediction accuracy (AUC) of resistance to imatinib (0.897) and dasatinib (0.996). For ABL1 isoform 2 (resistance to imatinib), the best combination was MNA descriptors of the 6th level, peptides form 15 amino acids (AUC value was 0.909). The prediction of possible drug-resistant AASs was made for dbSNP and gnomAD data. The six selected most probable imatinib-resistant AASs were additionally validated by molecular modeling and docking, which confirmed the possibility of resistance for the E334V and T392I variants. Full article

(This article belongs to the Special Issue Application Research of Bioinformatics in Human Diseases)

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23 pages, 4539 KiB

Open AccessArticle

A Noise-Tolerating Gene Association Network Uncovering an Oncogenic Regulatory Motif in Lymphoma Transcriptomics

by Wei-Quan Fang, Yu-Le Wu and Ming-Jing Hwang

Life 2023, 13(6), 1331; https://doi.org/10.3390/life13061331 - 6 Jun 2023

Viewed by 1253

Abstract

In cancer genomics research, gene expressions provide clues to gene regulations implicating patients’ risk of survival. Gene expressions, however, fluctuate due to noises arising internally and externally, making their use to infer gene associations, hence regulation mechanisms, problematic. Here, we develop a new regression approach to model gene association networks while considering uncertain biological noises. In a series of simulation experiments accounting for varying levels of biological noises, the new method was shown to be robust and perform better than conventional regression methods, as judged by a number of statistical measures on unbiasedness, consistency and accuracy. Application to infer gene associations in germinal-center B cells led to the discovery of a three-by-two regulatory motif gene expression and a three-gene prognostic signature for diffuse large B-cell lymphoma. Full article

(This article belongs to the Special Issue Application Research of Bioinformatics in Human Diseases)

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17 pages, 6497 KiB

Open AccessArticle

Integrated Computational Approaches for Inhibiting Sex Hormone-Binding Globulin in Male Infertility by Screening Potent Phytochemicals

by Suvro Biswas, Mohasana Akter Mita, Shamima Afrose, Md. Robiul Hasan, Md. Tarikul Islam, Md. Ashiqur Rahman, Mst. Jasmin Ara, Md. Bakhtiar Abid Chowdhury, Habibatun Naher Meem, Md. Mamunuzzaman, Tanvir Ahammad, Istiaq Uddin Ashik, Munjed M. Ibrahim, Mohammad Tarique Imam, Mohammad Akbar Hossain and Md. Abu Saleh

Life 2023, 13(2), 476; https://doi.org/10.3390/life13020476 - 9 Feb 2023

Cited by 5 | Viewed by 3579

Abstract

Male infertility is significantly influenced by the plasma-protein sex hormone-binding globulin (SHBG). Male infertility, erectile dysfunction, prostate cancer, and several other male reproductive system diseases are all caused by reduced testosterone bioavailability due to its binding to SHBG. In this study, we have identified 345 phytochemicals from 200 literature reviews that potentially inhibit severe acute respiratory syndrome coronavirus 2. Only a few studies have been done using the SARS-CoV-2 inhibitors to identify the SHBG inhibitor, which is thought to be the main protein responsible for male infertility. In virtual-screening and molecular-docking experiments, cryptomisrine, dorsilurin E, and isoiguesterin were identified as potential SHBG inhibitors with binding affinities of −9.2, −9.0, and −8.8 kcal/mol, respectively. They were also found to have higher binding affinities than the control drug anastrozole (−7.0 kcal/mol). In addition to favorable pharmacological properties, these top three phytochemicals showed no adverse effects in pharmacokinetic evaluations. Several molecular dynamics simulation profiles’ root-mean-square deviation, radius of gyration, root-mean-square fluctuation, hydrogen bonds, and solvent-accessible surface area supported the top three protein–ligand complexes’ better firmness and stability than the control drug throughout the 100 ns simulation period. These combinatorial drug-design approaches indicate that these three phytochemicals could be developed as potential drugs to treat male infertility. Full article

(This article belongs to the Special Issue Application Research of Bioinformatics in Human Diseases)

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17 pages, 5863 KiB

Open AccessArticle

Analysis of the Potential Relationship between Aging and Pulmonary Fibrosis Based on Transcriptome

by San Fu, Xiaoyan Tang, Yiming Xu, Xianrui Song, Xiuhui Qian, Yingying Hu and Mian Zhang

Life 2022, 12(12), 1961; https://doi.org/10.3390/life12121961 - 23 Nov 2022

Cited by 1 | Viewed by 2320

Abstract

Idiopathic pulmonary fibrosis (IPF) is an age-related interstitial lung disease with a high incidence in the elderly. Although many reports have shown that senescence can initiate pulmonary fibrosis, the relationship between aging and pulmonary fibrosis has not been explained systematically. In our study, young and old rats were intratracheally instilled with bleomycin (1 mg/kg), and the basic pathological indexes were determined using a commercial kit, hematoxylin, and eosin (H&E) and Masson’s Trichrome staining, immunohistochemistry, immunohistofluorescence, and q-PCR. Then, the lung tissues of rats were sequenced by next-generation sequencing for transcriptome analysis. Bioinformatics was performed to analyze the possible differences in the mechanism of pulmonary fibrosis between aged and young rats. Finally, the related cytokines were determined by q-PCR and ELISA. The results indicate that pulmonary fibrosis in old rats is more serious than that in young rats under the same conditions. Additionally, transcriptomic and bioinformatics analysis with experimental validation indicate that the differences in pulmonary fibrosis between old and young rats are mainly related to the differential expression of cytokines, extracellular matrix (ECM), and other important signaling pathways. In conclusion, aging mainly affects pulmonary fibrosis through the ECM–receptor interaction, immune response, and chemokines. Full article

(This article belongs to the Special Issue Application Research of Bioinformatics in Human Diseases)

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16 pages, 7587 KiB

Open AccessArticle

Multi-Omics Analysis Reveals Clinical Value and Possible Mechanisms of ATAD1 Down-Regulation in Human Prostate Adenocarcinoma

by Chun-Chi Chen, Pei-Yi Chu and Hung-Yu Lin

Life 2022, 12(11), 1742; https://doi.org/10.3390/life12111742 - 30 Oct 2022

Cited by 2 | Viewed by 2245

Abstract

Prostate adenocarcinoma (PRAD) is the most common histological subtype of prostate cancer. Post-treatment biochemical recurrence is a challenging issue. ATAD1 (ATPase Family AAA Domain Containing 1) plays a vital role in mitochondrial proteostasis and apoptosis activity, while its clinical value in PRAD and its impact on the tumor microenvironment (TME) remain unanswered. In this study, we aimed to investigate the clinical value and possible mechanisms of ATAD1 in PRAD via multi-omics analysis. Using cBioPortal, we confirmed that ATAD1 alteration was associated with gene expression and unfavorable DFS. Deep deletion predominantly occurred in PRAD. By integrating DriverDBv3 and GEPIA2, we noted ATAD1 downregulation in PRAD tissues compared to normal tissues, associated with unfavorable DFS in PRAD patients. DNA repair genes ATM, PARP1and BRCA2 had positive associations with ATAD1 expression. We found that the generalization value of ATAD1 could be applied to other cancers such as KIRC and UCEC. In addition, LinkedOmics identified that the functional involvement of ATAD1 participates in mitochondrial structure and cell cycle progression. Using TIMER analysis, we demonstrated that ATAD1 downregulation correlated with an immunosuppressive TME. Furthermore, we accessed a GSE55062 dataset on UALCAN and discovered the involvement of ERG-mediated transcriptional repression on ATAD1 downregulation. Cross-association screening of shATAD1 efficacy vs. altered mRNAs identified 190 perturbed mRNAs. Then, functional enrichment analysis using the Metascape omics tool recognized that shATAD1-perturbed mRNAs are primarily in charge of the activation of Wnt/β-catenin pathway and lipid metabolic processes. In conclusion, multi-omics results reveal that ATAD1 downregulation is a clinical biomarker for pathological diagnosis and prognosis for patients with PRAD. Reduced ATAD1 may be involved in the enhanced activity of mitochondria and cell cycle, as well as possibly shaping an immunosuppressive TME. ERG serves as an upstream transcriptional repressor of ATAD1. Downstream mechanisms of ATAD1 are involved in Wnt/β-catenin pathway and lipid metabolic processes. Full article

(This article belongs to the Special Issue Application Research of Bioinformatics in Human Diseases)

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14 pages, 4113 KiB

Open AccessArticle

Candidate Multi-Epitope Vaccine against Corona B.1.617 Lineage: In Silico Approach

by Mohamed G. Seadawy, Abdel Rahman N. Zekri, Aya A. Saeed, Emmanuel James San and Amr M. Ageez

Life 2022, 12(11), 1715; https://doi.org/10.3390/life12111715 - 27 Oct 2022

Cited by 5 | Viewed by 2176

Abstract

Various mutations have accumulated since the first genome sequence of SARS-CoV2 in 2020. Mutants of the virus carrying the D614G and P681R mutations in the spike protein are increasingly becoming dominant all over the world. The two mutations increase the viral infectivity and severity of the disease. This report describes an in silico design of SARS-CoV-2 multi-epitope carrying the spike D614G and P681R mutations. The designed vaccine harbors the D614G mutation that increases viral infectivity, fitness, and the P681R mutation that enhances the cleavage of S to S1 and S2 subunits. The designed multi-epitope vaccine showed an antigenic property with a value of 0.67 and the immunogenicity of the predicted vaccine was calculated and yielded 3.4. The vaccine construct is predicted to be non-allergenic, thermostable and has hydrophilic nature. The combination of the selected CTL and HTL epitopes in the vaccine resulted in 96.85% population coverage globally. Stable interactions of the vaccine with Toll-Like Receptor 4 were tested by docking studies. The multi-epitope vaccine can be a good candidate against highly infecting SARS-CoV-2 variants. Full article

(This article belongs to the Special Issue Application Research of Bioinformatics in Human Diseases)

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Review

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20 pages, 1074 KiB

Open AccessReview

Insights into Sex and Gender Differences in Brain and Psychopathologies Using Big Data

by Aura Zelco, Pattama Wapeesittipan and Anagha Joshi

Life 2023, 13(8), 1676; https://doi.org/10.3390/life13081676 - 2 Aug 2023

Cited by 5 | Viewed by 7080

Abstract

The societal implication of sex and gender (SG) differences in brain are profound, as they influence brain development, behavior, and importantly, the presentation, prevalence, and therapeutic response to diseases. Technological advances have enabled speed up identification and characterization of SG differences during development and in psychopathologies. The main aim of this review is to elaborate on new technological advancements, such as genomics, imaging, and emerging biobanks, coupled with bioinformatics analyses of data generated from these technologies have facilitated the identification and characterization of SG differences in the human brain through development and psychopathologies. First, a brief explanation of SG concepts is provided, along with a developmental and evolutionary context. We then describe physiological SG differences in brain activity and function, and in psychopathologies identified through imaging techniques. We further provide an overview of insights into SG differences using genomics, specifically taking advantage of large cohorts and biobanks. We finally emphasize how bioinformatics analyses of big data generated by emerging technologies provides new opportunities to reduce SG disparities in health outcomes, including major challenges. Full article

(This article belongs to the Special Issue Application Research of Bioinformatics in Human Diseases)

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