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1.
Contriving Multi-Epitope Subunit of Vaccine for COVID-19: Immunoinformatics Approaches.
Dong, R, Chu, Z, Yu, F, Zha, Y
Frontiers in immunology. 2020;:1784
Abstract
COVID-19 has recently become the most serious threat to public health, and its prevalence has been increasing at an alarming rate. The incubation period for the virus is ~1-14 days and all age groups may be susceptible to a fatality rate of about 5.9%. COVID-19 is caused by a novel single-stranded, positive (+) sense RNA beta coronavirus. The development of a vaccine for SARS-CoV-2 is an urgent need worldwide. Immunoinformatics approaches are both cost-effective and convenient, as in silico predictions can reduce the number of experiments needed. In this study, with the aid of immunoinformatics tools, we tried to design a multi-epitope vaccine that can be used for the prevention and treatment of COVID-19. The epitopes were computed by using B cells, cytotoxic T lymphocytes (CTL), and helper T lymphocytes (HTL) base on the proteins of SARS-CoV-2. A vaccine was devised by fusing together the B cell, HTL, and CTL epitopes with linkers. To enhance the immunogenicity, the β-defensin (45 mer) amino acid sequence, and pan-HLA DR binding epitopes (13aa) were adjoined to the N-terminal of the vaccine with the help of the EAAAK linker. To enable the intracellular delivery of the modeled vaccine, a TAT sequence (11aa) was appended to C-terminal. Linkers play vital roles in producing an extended conformation (flexibility), protein folding, and separation of functional domains, and therefore, make the protein structure more stable. The secondary and three-dimensional (3D) structure of the final vaccine was then predicted. Furthermore, the complex between the final vaccine and immune receptors (toll-like receptor-3 (TLR-3), major histocompatibility complex (MHC-I), and MHC-II) were evaluated by molecular docking. Lastly, to confirm the expression of the designed vaccine, the mRNA of the vaccine was enhanced with the aid of the Java Codon Adaptation Tool, and the secondary structure was generated from Mfold. Then we performed in silico cloning. The final vaccine requires experimental validation to determine its safety and efficacy in controlling SARS-CoV-2 infections.
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2.
Immunoinformatics guided rational design of a next generation multi epitope based peptide (MEBP) vaccine by exploring Zika virus proteome.
Shahid, F, Ashfaq, UA, Javaid, A, Khalid, H
Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases. 2020;:104199
Abstract
Zika virus (ZIKV) is an RNA virus that has spread through mosquito sting. Currently, no vaccine and antiviral medication available so far against ZIKV. Therefore, it has fostered a study to design MEBP vaccine enabling effective prevention against the ZIKV infection. In this study combination of immuno-informatics and molecular docking approach was used to constitute a MEBP vaccine. The ZIKV proteome was used for prediction of B-cell, T-cell (HTL & CTL) and IFN-γ epitopes. After prediction, highly antigenic and overlapping epitopes have been shortlisted which includes 14 CTL and 11 HTL epitopes that have been linked to the final peptide through AAY and GPGPG linkers respectively. An adjuvant at the N-end of the vaccine was added to improve the immunogenicity of the vaccine through the EAAAK linker. The final construct constitutes 435 amino acids after the addition of linkers and adjuvant. The existence of B-cell and IFN-γ epitopes affirms the humoral and cell-mediated immune responses acquired by the construct. Allergenicity, antigenicity and different physiochemical attributes of the vaccine were evaluated to assure its safety and immunogenicity profile. In fact, the construct was antigenic and non-allergenic. Docking was performed among vaccine and TLR-3 to evaluate the binding affinity and the molecular interaction. Finally, the construct was subjected to In silico cloning to confers the authenticity of its expression efficiency. However, the proposed construct need to be validate experimentally to ensure its safety and immunogenic profile.
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3.
Single Cell RNA Sequencing of Human Milk-Derived Cells Reveals Sub-Populations of Mammary Epithelial Cells with Molecular Signatures of Progenitor and Mature States: a Novel, Non-invasive Framework for Investigating Human Lactation Physiology.
Martin Carli, JF, Trahan, GD, Jones, KL, Hirsch, N, Rolloff, KP, Dunn, EZ, Friedman, JE, Barbour, LA, Hernandez, TL, MacLean, PS, et al
Journal of mammary gland biology and neoplasia. 2020;(4):367-387
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Abstract
Cells in human milk are an untapped source, as potential "liquid breast biopsies", of material for investigating lactation physiology in a non-invasive manner. We used single cell RNA sequencing (scRNA-seq) to identify milk-derived mammary epithelial cells (MECs) and their transcriptional signatures in women with diet-controlled gestational diabetes (GDM) with normal lactation. Methodology is described for coordinating milk collections with single cell capture and library preparation via cryopreservation, in addition to scRNA-seq data processing and analyses of MEC transcriptional signatures. We comprehensively characterized 3740 cells from milk samples from two mothers at two weeks postpartum. Most cells (>90%) were luminal MECs (luMECs) expressing lactalbumin alpha and casein beta and positive for keratin 8 and keratin 18. Few cells were keratin 14+ basal MECs and a small immune cell population was present (<10%). Analysis of differential gene expression among clusters identified six potentially distinct luMEC subpopulation signatures, suggesting the potential for subtle functional differences among luMECs, and included one cluster that was positive for both progenitor markers and mature milk transcripts. No expression of pluripotency markers POU class 5 homeobox 1 (POU5F1, encoding OCT4) SRY-box transcription factor 2 (SOX2) or nanog homeobox (NANOG), was observed. These observations were supported by flow cytometric analysis of MECs from mature milk samples from three women with diet-controlled GDM (2-8 mo postpartum), indicating a negligible basal/stem cell population (epithelial cell adhesion molecule (EPCAM)-/integrin subunit alpha 6 (CD49f)+, 0.07%) and a small progenitor population (EPCAM+/CD49f+, 1.1%). We provide a computational framework for others and future studies, as well as report the first milk-derived cells to be analyzed by scRNA-seq. We discuss the clinical potential and current limitations of using milk-derived cells as material for characterizing human mammary physiology.
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Human microbiome: an academic update on human body site specific surveillance and its possible role.
Dekaboruah, E, Suryavanshi, MV, Chettri, D, Verma, AK
Archives of microbiology. 2020;(8):2147-2167
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Abstract
Human body is inhabited by vast number of microorganisms which form a complex ecological community and influence the human physiology, in the aspect of both health and diseases. These microbes show a relationship with the human immune system based on coevolution and, therefore, have a tremendous potential to contribute to the metabolic function, protection against the pathogen and in providing nutrients and energy. However, of these microbes, many carry out some functions that play a crucial role in the host physiology and may even cause diseases. The introduction of new molecular technologies such as transcriptomics, metagenomics and metabolomics has contributed to the upliftment on the findings of the microbiome linked to the humans in the recent past. These rapidly developing technologies are boosting our capacity to understand about the human body-associated microbiome and its association with the human health. The highlights of this review are inclusion of how to derive microbiome data and the interaction between human and associated microbiome to provide an insight on the role played by the microbiome in biological processes of the human body as well as the development of major human diseases.
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INeo-Epp: A Novel T-Cell HLA Class-I Immunogenicity or Neoantigenic Epitope Prediction Method Based on Sequence-Related Amino Acid Features.
Wang, G, Wan, H, Jian, X, Li, Y, Ouyang, J, Tan, X, Zhao, Y, Lin, Y, Xie, L
BioMed research international. 2020;:5798356
Abstract
In silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g., proteasomal cleavage, transporter associated with antigen processing (TAP), and major histocompatibility complex (MHC) combination. To date, however, the mechanism for the immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties, and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including the amino acid physicochemical property of the epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score, and frequency score for an immunogenic peptide. Subsequently, a random forest classifier for T-cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC = 0.81, external validation data set AUC = 0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called "neoantigens." Based on mutation information and sequence-related amino acid characteristics, a prediction model of a neoantigen was established as well (the optimal AUC = 0.78). Further, an easy-to-use web-based tool "INeo-Epp" was developed for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.
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Investigation of Potential Genetic Biomarkers and Molecular Mechanism of Ulcerative Colitis Utilizing Bioinformatics Analysis.
Zhang, J, Wang, X, Xu, L, Zhang, Z, Wang, F, Tang, X
BioMed research international. 2020;:4921387
Abstract
OBJECTIVES To reveal the molecular mechanisms of ulcerative colitis (UC) and provide potential biomarkers for UC gene therapy. METHODS We downloaded the GSE87473 microarray dataset from the Gene Expression Omnibus (GEO) and identified the differentially expressed genes (DEGs) between UC samples and normal samples. Then, a module partition analysis was performed based on a weighted gene coexpression network analysis (WGCNA), followed by pathway and functional enrichment analyses. Furthermore, we investigated the hub genes. At last, data validation was performed to ensure the reliability of the hub genes. RESULTS Between the UC group and normal group, 988 DEGs were investigated. The DEGs were clustered into 5 modules using WGCNA. These DEGs were mainly enriched in functions such as the immune response, the inflammatory response, and chemotaxis, and they were mainly enriched in KEGG pathways such as the cytokine-cytokine receptor interaction, chemokine signaling pathway, and complement and coagulation cascades. The hub genes, including dual oxidase maturation factor 2 (DUOXA2), serum amyloid A (SAA) 1 and SAA2, TNFAIP3-interacting protein 3 (TNIP3), C-X-C motif chemokine (CXCL1), solute carrier family 6 member 14 (SLC6A14), and complement decay-accelerating factor (CD antigen CD55), were revealed as potential tissue biomarkers for UC diagnosis or treatment. CONCLUSIONS This study provides supportive evidence that DUOXA2, A-SAA, TNIP3, CXCL1, SLC6A14, and CD55 might be used as potential biomarkers for tissue biopsy of UC, especially SLC6A14 and DUOXA2, which may be new targets for UC gene therapy. Moreover, the DUOX2/DUOXA2 and CXCL1/CXCR2 pathways might play an important role in the progression of UC through the chemokine signaling pathway and inflammatory response.
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In silico identification of epitopes present in human heat shock proteins (HSPs) overexpressed by tumour cells.
Marchan, J
Journal of immunological methods. 2019;:34-45
Abstract
Although many of heat shock proteins (HSPs) are crucial in homeostasis due to their role in maintaining cellular proteostasis by the integration of two pivotal processes-folding and degradation, several decades of cancer proteomics suggest that HSPs may improve cancer establishment and progression. Therefore, it is imperative to explore how these molecules impact patient outcomes and whether their interaction with the immune systems improves the protumour or antitumour environment. Here, using an immunoinformatics approach were investigated the best probable epitopes from ten HSPs (HSP90α, HSP90β, HSPA1A, HSPA1L, HSPA2, HSPA5, HSPA6, HSPB1, HSPB5 and HSP60/HSP10). To achieve this aim, antigenicity, immunogenicity (prediction of continuous and discontinuous B cell epitopes, binding peptides to HLA class I and HLA class II, and overlapping epitopes), analysis of conservancy and population coverage, and prediction of IgE epitopes were evaluated. According to the physicochemical properties used for their prediction (hydrophilicity, flexibility, accessibility and antigenicity propensity), ten continuous epitopes (one per HSPs) were considered as the best and also several regions of each molecule were identified as B discontinuous epitopes. Interestingly, peptides of HSP90β, HSPA2, HSPB1, and HSPB5 were predicted as both continuous and discontinuous B cell epitopes. For all the HSPs evaluated were identified potential overlapping epitopes ("NTFYSNKEI", "TTYSCVGVF", "TADRWRVSL", "VKHFSPEEL" and "CEFQDAYVL"). Moreover, these peptides were negative for IgE epitopes and showed a large coverage in the human population (HLA-A*02, HLA-B*15, HLA-C*03, and HLA-C*12). Taken together, these data indicate that such epitopes may activate both the humoral and cell-mediated response, and thus serve as therapeutic targets for cancer. However, it must be assessed their efficacy and safety in vitro and in vivo before their translation in clinical trials.
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Exploring NS3/4A, NS5A and NS5B proteins to design conserved subunit multi-epitope vaccine against HCV utilizing immunoinformatics approaches.
Ikram, A, Zaheer, T, Awan, FM, Obaid, A, Naz, A, Hanif, R, Paracha, RZ, Ali, A, Naveed, AK, Janjua, HA
Scientific reports. 2018;(1):16107
Abstract
Hepatitis C virus (HCV) vaccines, designed to augment specific T-cell responses, have been designated as an important aspect of effective antiviral treatment. However, despite the current satisfactory progress of these vaccines, extensive past efforts largely remained unsuccessful in mediating clinically relevant anti-HCV activity in humans. In this study, we used a series of immunoinformatics approaches to propose a multiepitope vaccine against HCV by prioritizing 16 conserved epitopes from three viral proteins (i.e., NS34A, NS5A, and NS5B). The prioritised epitopes were tested for their possible antigenic combinations with each other along with linker AAY using structural modelling and epitope-epitope interactions analysis. An adjuvant (β-defensin) at the N-terminal of the construct was added to enhance the immunogenicity of the vaccine construct. Molecular dynamics (MD) simulation revealed the most stable structure of the proposed vaccine. The designed vaccine is potentially antigenic in nature and can form stable and significant interactions with Toll-like receptor 3 and Toll-like receptor 8. The proposed vaccine was also subjected to an in silico cloning approach, which confirmed its expression efficiency. These analyses suggest that the proposed vaccine can elicit specific immune responses against HCV; however, experimental validation is required to confirm the safety and immunogenicity profile of the proposed vaccine construct.
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9.
PRWHMDA: Human Microbe-Disease Association Prediction by Random Walk on the Heterogeneous Network with PSO.
Wu, C, Gao, R, Zhang, D, Han, S, Zhang, Y
International journal of biological sciences. 2018;(8):849-857
Abstract
Microorganisms resided in human body play a vital role in metabolism, immune defense, nutrition absorption, cancer control and protection against pathogen colonization. The changes of microbial communities can cause human diseases. Based on the known microbe-disease association, we presented a novel computational model employing Random Walking with Restart optimized by Particle Swarm Optimization (PSO) on the heterogeneous interlinked network of Human Microbe-Disease Associations (PRWHMDA) (see Figure 1). Based on the known human microbe-disease associations, we constructed the heterogeneous interlinked network with Cosine similarity. The extended random walk with restart (RWR) method was derived to get the potential microbe-disease associations. PSO was utilized to get the optimal parameters of RWR. To evaluate the prediction effectiveness, we performed leave one out cross validation (LOOCV) and 5-fold cross validation (CV), which got the AUC (The area under ROC curve) of 0.915 (LOOCV) and the average AUCs of 0.8875 ± 0.0046 (5-fold CV). Moreover, we carried out three case studies of asthma, inflammatory bowel disease (IBD) and type 1 diabetes (T1D) for the further evaluation. The result showed that 10, 10 and 9 of top-10 predicted microbes were verified by previously published experimental results, respectively. It is anticipated that PRWHMDA can be effective to identify the disease-related microbes and maybe helpful to disclose the relationship between microorganisms and their human host.
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10.
Population-specific design of de-immunized protein biotherapeutics.
Schubert, B, Schärfe, C, Dönnes, P, Hopf, T, Marks, D, Kohlbacher, O
PLoS computational biology. 2018;(3):e1005983
Abstract
Immunogenicity is a major problem during the development of biotherapeutics since it can lead to rapid clearance of the drug and adverse reactions. The challenge for biotherapeutic design is therefore to identify mutants of the protein sequence that minimize immunogenicity in a target population whilst retaining pharmaceutical activity and protein function. Current approaches are moderately successful in designing sequences with reduced immunogenicity, but do not account for the varying frequencies of different human leucocyte antigen alleles in a specific population and in addition, since many designs are non-functional, require costly experimental post-screening. Here, we report a new method for de-immunization design using multi-objective combinatorial optimization. The method simultaneously optimizes the likelihood of a functional protein sequence at the same time as minimizing its immunogenicity tailored to a target population. We bypass the need for three-dimensional protein structure or molecular simulations to identify functional designs by automatically generating sequences using probabilistic models that have been used previously for mutation effect prediction and structure prediction. As proof-of-principle we designed sequences of the C2 domain of Factor VIII and tested them experimentally, resulting in a good correlation with the predicted immunogenicity of our model.