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29 pages, 2623 KiB  
Article
Stability and Optimality Criteria for an SVIR Epidemic Model with Numerical Simulation
by Halet Ismail, Amar Debbouche, Soundararajan Hariharan, Lingeshwaran Shangerganesh and Stanislava V. Kashtanova
Mathematics 2024, 12(20), 3231; https://doi.org/10.3390/math12203231 (registering DOI) - 15 Oct 2024
Viewed by 262
Abstract
The mathematical modeling of infectious diseases plays a vital role in understanding and predicting disease transmission, as underscored by recent global outbreaks; to delve deep into the dynamic of infectious disease considering latent period presciently is inevitable as it bridges the gap between [...] Read more.
The mathematical modeling of infectious diseases plays a vital role in understanding and predicting disease transmission, as underscored by recent global outbreaks; to delve deep into the dynamic of infectious disease considering latent period presciently is inevitable as it bridges the gap between realistic nature and mathematical modeling. This study extended the classical Susceptible–Infected–Recovered (SIR) model by incorporating vaccination strategies during incubation. We introduced multiple time delays to an account incubation period to capture realistic disease dynamics better. The model is formulated as a system of delay differential equations that describe the transmission dynamics of diseases such as polio or COVID-19, or diseases for which vaccination exists. Critical aspects of the study include proving the positivity of the model’s solutions, calculating the basic reproduction number (R0) using next-generation matrix theory, and identifying disease-free and endemic equilibrium points. The local stability of these equilibria is then analyzed using the Routh–Hurwitz criterion. Due to the complexity introduced by the delay components, we examine the stability by studying the roots of a fourth-degree exponential polynomial. The effects of educational campaigns and vaccination efficacy are also investigated as control measures. Furthermore, an optimization problem is formulated, based on Pontryagin’s maximum principle, to minimize the number of infections and associated intervention costs. Numerical simulations of the delay differential equations are conducted, and a modified Runge–Kutta method with delays is used to solve the optimal control problem. Finally, we present a few simulation results to illustrate the analytical findings. Full article
Show Figures

Figure 1

Figure 1
<p>Illustrative diagram of SVIR model incorporating time delay.</p>
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<p>Plot represents the sensitivity of the parameters in the BRN of infection model.</p>
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<p>Impact of <math display="inline"><semantics> <mi>β</mi> </semantics></math> and <math display="inline"><semantics> <mi>δ</mi> </semantics></math> on <math display="inline"><semantics> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> </semantics></math>.</p>
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<p>Impact of <math display="inline"><semantics> <mi>β</mi> </semantics></math> and <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> on <math display="inline"><semantics> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> </semantics></math>.</p>
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<p>Impact of <math display="inline"><semantics> <mi>δ</mi> </semantics></math> and <math display="inline"><semantics> <mi>α</mi> </semantics></math> on <math display="inline"><semantics> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> </semantics></math>.</p>
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<p>Impact of <span class="html-italic">b</span> and <math display="inline"><semantics> <mi>ρ</mi> </semantics></math> on <math display="inline"><semantics> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> </semantics></math>.</p>
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<p>Impact of <math display="inline"><semantics> <mo>Λ</mo> </semantics></math> and <math display="inline"><semantics> <mi>θ</mi> </semantics></math> on <math display="inline"><semantics> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> </semantics></math>.</p>
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<p>Plots (<b>a</b>–<b>d</b>) represents the populations <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>,</mo> <mi>V</mi> <mo>,</mo> <mi>I</mi> <mo>,</mo> <mi>R</mi> </mrow> </semantics></math> as a function of <span class="html-italic">t</span> with <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mn>1</mn> </msub> <mo>=</mo> <msub> <mi>τ</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> for different vaccine levels, respectively.</p>
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<p>Plot represents infectious population under (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.7609</mn> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>1.5037</mn> <mo>&gt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.19</mn> </mrow> </semantics></math>, fixed <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>, varied <math display="inline"><semantics> <msub> <mi>τ</mi> <mn>1</mn> </msub> </semantics></math>.</p>
Full article ">Figure 10
<p>Plot represents infectious population under (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.7609</mn> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>1.5037</mn> <mo>&gt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.19</mn> </mrow> </semantics></math>, fixed <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>, varied <math display="inline"><semantics> <msub> <mi>τ</mi> <mn>2</mn> </msub> </semantics></math>.</p>
Full article ">Figure 11
<p>Plot represents susceptible population under (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.7609</mn> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>1.5037</mn> <mo>&gt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.19</mn> </mrow> </semantics></math>, fixed <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>, varied <math display="inline"><semantics> <msub> <mi>τ</mi> <mn>1</mn> </msub> </semantics></math>.</p>
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<p>Plot represents vaccination population under (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.7609</mn> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>1.5037</mn> <mo>&gt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.19</mn> </mrow> </semantics></math>, fixed <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>, varied <math display="inline"><semantics> <msub> <mi>τ</mi> <mn>1</mn> </msub> </semantics></math>.</p>
Full article ">Figure 13
<p>Plot represents recovered population under (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.7609</mn> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>1.5037</mn> <mo>&gt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.19</mn> </mrow> </semantics></math>, fixed <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>, varied <math display="inline"><semantics> <msub> <mi>τ</mi> <mn>1</mn> </msub> </semantics></math>.</p>
Full article ">Figure 14
<p>Plot represents susceptible population under (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.7609</mn> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>1.5037</mn> <mo>&gt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.19</mn> </mrow> </semantics></math>, fixed <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>, varied <math display="inline"><semantics> <msub> <mi>τ</mi> <mn>2</mn> </msub> </semantics></math>.</p>
Full article ">Figure 15
<p>Plot represents vaccination population under (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.7609</mn> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>1.5037</mn> <mo>&gt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.19</mn> </mrow> </semantics></math>, fixed <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>, varied <math display="inline"><semantics> <msub> <mi>τ</mi> <mn>2</mn> </msub> </semantics></math>.</p>
Full article ">Figure 16
<p>Recovered under (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.7609</mn> <mo>&lt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.6</mn> </mrow> </semantics></math> and (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>1.5037</mn> <mo>&gt;</mo> <mn>1</mn> </mrow> </semantics></math> with <math display="inline"><semantics> <mrow> <mi>b</mi> <mo>=</mo> <mn>0.19</mn> </mrow> </semantics></math>, fixed <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>, varied <math display="inline"><semantics> <msub> <mi>τ</mi> <mn>2</mn> </msub> </semantics></math>.</p>
Full article ">Figure 17
<p>Plots (<b>a</b>–<b>d</b>) represent the impact of control variables <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mi>v</mi> <mn>1</mn> </msub> <mo>,</mo> <mspace width="3.33333pt"/> <msub> <mi>v</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </semantics></math> in susceptible humans <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>S</mi> <mo>)</mo> </mrow> </semantics></math>, vaccinated humans <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>V</mi> <mo>)</mo> </mrow> </semantics></math>, infected <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>I</mi> <mo>)</mo> </mrow> </semantics></math>, and recovery <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </semantics></math>, respectively, over time <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>; here, <math display="inline"><semantics> <mrow> <msub> <mi>τ</mi> <mn>1</mn> </msub> <mo>=</mo> <msub> <mi>τ</mi> <mn>2</mn> </msub> <mo>=</mo> <mn>10</mn> <mo>.</mo> </mrow> </semantics></math></p>
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<p>Plots <math display="inline"><semantics> <mrow> <mo>(</mo> <mi mathvariant="bold">a</mi> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>(</mo> <mi mathvariant="bold">b</mi> <mo>)</mo> </mrow> </semantics></math> represents the values of the control variables <math display="inline"><semantics> <msub> <mi>v</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>v</mi> <mn>2</mn> </msub> </semantics></math> as functions of time <span class="html-italic">t</span> for various delay values respectively.</p>
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11 pages, 3085 KiB  
Article
Partial Sequence Analysis of Commercial Peste des Petits Ruminants Vaccines Produced in Africa
by Boubacar Barry, Yebechaye Tessema, Hassen Gelaw, Cisse Rahamatou Moustapha Boukary, Baziki Jean de Dieu, Melesse Ayelet Gelagay, Ethel Chitsungo, Richard Rayson Sanga, Gbolahanmi Akinola Oladosu, Nick Nwankpa and S. Charles Bodjo
Vet. Sci. 2024, 11(10), 500; https://doi.org/10.3390/vetsci11100500 - 13 Oct 2024
Viewed by 823
Abstract
Peste des petits ruminants virus (PPRV), which is the only member of the Morbillivirus caprinae species and belongs to the genus Morbillivirus within the Paramyxoviridae family, causes the highly contagious viral sickness “Peste des petits ruminants (PPR).” PPR is of serious economic significance [...] Read more.
Peste des petits ruminants virus (PPRV), which is the only member of the Morbillivirus caprinae species and belongs to the genus Morbillivirus within the Paramyxoviridae family, causes the highly contagious viral sickness “Peste des petits ruminants (PPR).” PPR is of serious economic significance for small ruminant production, particularly in Africa. Control of this critical disease depends highly on successful vaccination against the PPRV. An in-depth understanding of the genetic evolution of the live-attenuated PPR vaccine Nigeria 75/1 strain used in Africa is essential for the successful eradication of this disease by 2030. Therefore, this study investigated the possible genetic evolution of the PPR vaccine produced by various African laboratories compared with the master seed available at AU-PANVAC. RT-PCR was performed to amplify a segment of the hypervariable C-terminal part of the nucleoprotein (N) from commercial batches of PPR vaccine Nigeria 75/1 strain. The sequences were analyzed, and 100% nucleotide sequence identity was observed between the master seed and vaccines produced. The results of this study indicate the genetic stability of the PPR vaccine from the Nigeria 75/1 strain over decades and that the vaccine production process used by different manufacturers did not contribute to the emergence of mutations in the vaccine strain. Full article
(This article belongs to the Section Veterinary Microbiology, Parasitology and Immunology)
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Figure 1

Figure 1
<p>Gel electrophoresis results of RT-PCR with PPR vaccine samples (1 to 45) from 10 Manufacturers, (1–10) from Manufacturer 01 (NVI), (11–12) from Manufacturer 02 (KEVEVAPI), (13–20) from Manufacturer 03 (LANAVET), (21–24) from Manufacturer 04 (MCI), (25–30) from Manufacturer 05 (ISRA), (31–37) from Manufacturer 06 (LCV), (38–40) from Manufacturer 07 (BVI), (41–42) from Manufacturer 08 (HESTER Tanzania), (43) from Manufacturer 09 (NVRI), (44) from Manufacturer 10 (NAPHL), and (45) the master seed (master seed, Vero 78), MW: 100 bp molecular weight marker, PC: AU-PANVAC PPR vaccine positive control, P: positive control, and N: negative control. The required band amplification with primers NP3 and NP4 is at 351 bp.</p>
Full article ">Figure 2
<p>(<b>a</b>) Multiple sequence alignment of 287-bp sequence in the C-terminus region, corresponding to nucleotides 1283 to 1522 of the N gene generated with PPR vaccine samples using Clustal Omega &lt; EMBL-EBI. Data have shown sequence homology of 100%. (<b>b</b>) Multiple sequence alignment of 287-bp sequence in the C-terminus region, corresponding to nucleotides 1253 to 1570 of the N gene generated with PPR vaccine samples using Clustal Omega &lt; EMBL-EBI. Data have shown a sequence homology of 100.</p>
Full article ">Figure 2 Cont.
<p>(<b>a</b>) Multiple sequence alignment of 287-bp sequence in the C-terminus region, corresponding to nucleotides 1283 to 1522 of the N gene generated with PPR vaccine samples using Clustal Omega &lt; EMBL-EBI. Data have shown sequence homology of 100%. (<b>b</b>) Multiple sequence alignment of 287-bp sequence in the C-terminus region, corresponding to nucleotides 1253 to 1570 of the N gene generated with PPR vaccine samples using Clustal Omega &lt; EMBL-EBI. Data have shown a sequence homology of 100.</p>
Full article ">Figure 3
<p>Alignment of the generated consensus partial sequence of N gene (287 bp: nucleotides 1283 to 1448) with two published sequences of PPR virus Nigeria 75/1 strain (Genbank KY628761 and Diallo, 1994) using Clustal Omega &lt; EMBL-EBI.</p>
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20 pages, 13676 KiB  
Article
In Silico Identification of Potential Inhibitors of SARS-CoV-2 Main Protease (Mpro)
by Manuel Alejandro Hernández-Serda, Víctor H. Vázquez-Valadez, Pablo Aguirre-Vidal, Nathan M. Markarian, José L. Medina-Franco, Luis Alfonso Cardenas-Granados, Aldo Yoshio Alarcón-López, Pablo A. Martínez-Soriano, Ana María Velázquez-Sánchez, Rodolfo E. Falfán-Valencia, Enrique Angeles and Levon Abrahamyan
Pathogens 2024, 13(10), 887; https://doi.org/10.3390/pathogens13100887 - 11 Oct 2024
Viewed by 831
Abstract
The ongoing Coronavirus Disease 19 (COVID-19) pandemic has had a profound impact on the global healthcare system. As the SARS-CoV-2 virus, responsible for this pandemic, continues to spread and develop mutations in its genetic material, new variants of interest (VOIs) and variants of [...] Read more.
The ongoing Coronavirus Disease 19 (COVID-19) pandemic has had a profound impact on the global healthcare system. As the SARS-CoV-2 virus, responsible for this pandemic, continues to spread and develop mutations in its genetic material, new variants of interest (VOIs) and variants of concern (VOCs) are emerging. These outbreaks lead to a decrease in the efficacy of existing treatments such as vaccines or drugs, highlighting the urgency of new therapies for COVID-19. Therefore, in this study, we aimed to identify potential SARS-CoV-2 antivirals using a virtual screening protocol and molecular dynamics simulations. These techniques allowed us to predict the binding affinity of a database of compounds with the virus Mpro protein. This in silico approach enabled us to identify twenty-two chemical structures from a public database (QSAR Toolbox Ver 4.5 ) and ten promising molecules from our in-house database. The latter molecules possess advantageous qualities, such as two-step synthesis, cost-effectiveness, and long-lasting physical and chemical stability. Consequently, these molecules can be considered as promising alternatives to combat emerging SARS-CoV-2 variants. Full article
(This article belongs to the Section Viral Pathogens)
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Figure 1

Figure 1
<p>General workflow diagram.</p>
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<p>Structure of the SARS-CoV-2 M<sup>pro</sup> protein. The possible interaction sites are colored green, and the rest of the protein is blue. Image created in the Molecular Operating Environment, MOE 2022.02.</p>
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<p>Potential binding site evaluated by SiteFinder. The size of the sphere represents how tightly packed the atoms are in the receptor; the larger the volume, the more accessible the atoms become. Spheres are colored by hydrophobic (gray) and hydrophilic (red). Image generated in MOE, 2022.02.</p>
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<p>M<sup>pro</sup> 7TOB potential interaction site, in green, defined by “Proteins<span class="html-italic">Plus</span>” online server.</p>
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<p>Consensus amino acids belonging to M<sup>pro</sup> 7TOB site identified with DoGSiteScorer and SiteFinder from MOE 2022 tools.</p>
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<p>The crystal structure of M<sup>pro</sup>. Depiction of the M<sup>pro</sup> amino acid substitutions in the SARS-CoV-2 variants of concern. The purple residue represents the K90R substitution present in the beta B.1.351 variant, whereas the green residue represents the P132H substitution present in the Omicron variants BA.1, BA.2, BA.4, BA.5, BA.2.12.1, BA.2.75, BQ.1, and XBB. Defining amino acid changes are those that appear at the phylogenetic root of a variant. Figure made with BioRender with a purchased license.</p>
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<p>Grid of the 7TOB protein of SARS-CoV-2 in which the area where interactions with ligands were calculated is presented.</p>
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<p>M<sup>pro</sup>–Ensitrelvir complex active site. The direct protein–ligand interactions are represented in the dotted line. Three-dimensional (<b>left</b>) and two-dimensional (<b>right</b>) representations.</p>
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<p>M<sup>pro</sup>–Atazanavir complex active site. The direct protein–ligand interactions are represented in the dotted line. Three-dimensional (<b>left</b>) and two-dimensional (<b>right</b>) representations.</p>
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<p>M<sup>pro</sup>–LQM 778 complex active site. The direct protein–ligand interactions are represented in the dotted line. Three-dimensional (<b>left</b>) and two-dimensional (<b>right</b>) representations.</p>
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<p>RMSD of M<sup>pro</sup> systems: apo-form (in blue) and with the ligands M<sup>pro</sup>L6 (in red) and LQM 778 (in yellow).</p>
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<p>RMSF of the M<sup>pro</sup> protein in its apo-form in blue, compared to the ligands M<sup>pro</sup>L6 in red and LQM 778 in purple.</p>
Full article ">Figure 13
<p>Radius of gyration of the ligands M<sup>pro</sup>L6 and LQM 778 and the apo-protein M<sup>pro</sup>.</p>
Full article ">
16 pages, 4964 KiB  
Article
Innovative Cancer Immunotherapy with MAGE-A3 mRNA Cancer Vaccines
by Kangchan Choi, Hyorim Jeong, Do Hyun Lee, Ji Won Lee, Ju-Eun Hong, Jin Ee Baek and Yong Serk Park
Cancers 2024, 16(19), 3428; https://doi.org/10.3390/cancers16193428 - 9 Oct 2024
Viewed by 658
Abstract
Cancer causes over 10 million deaths annually worldwide and remains a significant global health challenge. This study investigated advanced immunotherapy strategies, focusing on mRNA vaccines that target tumor-specific antigens to activate the immune system. We developed a novel mRNA vaccine using O,O′-dimyristyl-N-lysyl aspartate [...] Read more.
Cancer causes over 10 million deaths annually worldwide and remains a significant global health challenge. This study investigated advanced immunotherapy strategies, focusing on mRNA vaccines that target tumor-specific antigens to activate the immune system. We developed a novel mRNA vaccine using O,O′-dimyristyl-N-lysyl aspartate (DMKD) to improve stability and phosphatidylserine (PS) to enhance antigen presentation to immune cells. This vaccine, containing melanoma-associated antigen A3 (MAGE-A3) mRNA encapsulated within lipid nanoparticles (LNPs), was evaluated for its therapeutic potential against colorectal cancer. Our findings demonstrated that MAGE-A3 mRNA-containing DMKD-PS LNPs significantly reduced tumor size and weight, effectively combating metastatic cancer. The vaccine elicited a robust immune response, increasing specific immunoglobulin and cytokine levels without causing histotoxicity in major organs. These results confirm that the DMKD-PS-based MAGE-A3 mRNA vaccine holds promise for cancer prevention and treatment. Full article
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Figure 1

Figure 1
<p>Melanoma-associated antigen A3 (MAGE-A3) mRNA synthesis. (<b>A</b>) Utilizing the IVT method for synthesizing GFP mRNA, the entire MAGE-A3 coding sequence was incorporated into the plasmid via subcloning. (<b>B</b>) Following sequencing and gene alignment, successful insertion of MAGE-A3 was confirmed.</p>
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<p>Relative expression of MAGE-A3 after in vitro mRNA transfection. (<b>A</b>) RNA and protein expression following transfection with various prepared LNPs and MAGE-A3 mRNA measured for 72 h. (<b>B</b>) Stability of varied LNPs/MAGE-A3 mRNA complexes refrigerated for 16 weeks was evaluated in terms of RNA transcription and protein production.</p>
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<p>Relative expression of MAGE-A3 after in vitro mRNA transfection. (<b>A</b>) RNA and protein expression following transfection with various prepared LNPs and MAGE-A3 mRNA measured for 72 h. (<b>B</b>) Stability of varied LNPs/MAGE-A3 mRNA complexes refrigerated for 16 weeks was evaluated in terms of RNA transcription and protein production.</p>
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<p>Preventive effects of MAGE-A3 mRNA-containing LNPs on tumor growth. (<b>A</b>) Experimental design of immunization and challenge of CT26 tumor cells in mice. The changes in (<b>B</b>) tumor size and (<b>C</b>) body weight were monitored for 28 d. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Histopathological analysis of major internal organs and tumors in mice treated with the mRNA anticancer vaccine. (<b>A</b>) On the 28th day post-inoculation, major organs were harvested from tumor-bearing mice and processed for histopathological examination. Magnification: ×100 (scale bar = 100 μm). (<b>B</b>) Tumor tissues extracted from vaccinated mice were analyzed immunohistochemically using an antibody specific to MAGE-A3. Magnification: ×200 (scale bar = 100 μm).</p>
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<p>Survival of mice immunized with MAGE-A3 mRNA and then challenged with tumor cells. (<b>A</b>) Schematic image of immunized mice were intravenously injected with CT26 cells to artificially induce a tumor metastasis model. (<b>B</b>) Survival rate of the mice vaccinated with MAGE-A3 mRNA in various LNPs were intravenously injected with CT26 cells to induce tumor metastasis artificially. (<b>C</b>) Entire livers and lungs with tumors were observed before (<b>upper</b>) and after (<b>bottom</b>) fixation in Bouin’s solution with picric acid. Tumors were identified and marked with white arrows. Tumors typically presented as dark red or white nodules and appeared as bright-yellow nodules upon fixation with Bouin’s solution. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The in vivo anticancer treatment efficacy of MAGE-A3 mRNA-containing LNPs. (<b>A</b>) A schematic illustration providing an overview of the experimental design. (<b>B</b>) Tumor growth was monitored twice a week after trice injection of the mRNA vaccines. (<b>C</b>) At the endpoint of the experiment, photographs of the sacrificed tumor-bearing mice and the excised tumors were captured, visually documenting the treatment outcomes. (<b>D</b>,<b>E</b>) The body weight of the mice was measured throughout the experimental period. ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>The in vivo anticancer treatment efficacy of MAGE-A3 mRNA-containing LNPs. (<b>A</b>) A schematic illustration providing an overview of the experimental design. (<b>B</b>) Tumor growth was monitored twice a week after trice injection of the mRNA vaccines. (<b>C</b>) At the endpoint of the experiment, photographs of the sacrificed tumor-bearing mice and the excised tumors were captured, visually documenting the treatment outcomes. (<b>D</b>,<b>E</b>) The body weight of the mice was measured throughout the experimental period. ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Histopathological examination of major internal organs and tumors of mice injected with MAGE-A3 mRNA vaccine. (<b>A</b>) Twenty-two days after the first treatment with MAGE-A3 mRNA vaccines, major organs were collected from the tumor-bearing mice. The organs were paraffin-embedded, sectioned, and subjected to H&amp;E staining for histopathological analysis. Magnification: ×100 (scale bar = 100 μm). (<b>B</b>) Tumor tissues were immunohistochemically stained using an anti-MAGE-A3 antibody and examined under a light microscope for detailed analysis. Magnification: ×200 (scale bar = 100 μm).</p>
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<p>Immunoglobulin subtyping and cytokine profiling following MAGE-A3 mRNA anticancer vaccination. (<b>A</b>) A schematic representation of blood collection after the injection of CT26 cell lysate into the tail vein of an immunized mouse. (<b>B</b>) Immunoglobulin subtypes and (<b>C</b>) cytokines, specifically IL-4 and IFN-γ, were quantified a week later.</p>
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<p>Cytokine profiling following MAGE-A3 mRNA anticancer vaccination from immunized splenocyte culture media. (<b>A</b>) A schematic representation of the culture process of the spleen derived from an immunized mouse and the subsequent cytokine profiling. (<b>B</b>) The cytokines IL-4 and IFN-γ in immunized splenocyte culture media were quantified. *** <span class="html-italic">p</span> &lt; 0.001, ns—not specified.</p>
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16 pages, 1948 KiB  
Article
Comprehensive Optimization of a Freeze-Drying Process Achieving Enhanced Long-Term Stability and In Vivo Performance of Lyophilized mRNA-LNPs
by Teresa Alejo, Alfonso Toro-Córdova, Laura Fernández, Andrea Rivero, Andrei Mihai Stoian, Luna Pérez, Victor Navarro, Juan Martínez-Oliván and Diego de Miguel
Int. J. Mol. Sci. 2024, 25(19), 10603; https://doi.org/10.3390/ijms251910603 - 1 Oct 2024
Viewed by 889
Abstract
The success of mRNA vaccines against SARS-CoV-2 has prompted interest in mRNA-based pharmaceuticals due to their rapid production, adaptability, and safety. Despite these advantages, the inherent instability of mRNA and its rapid degradation in vivo underscores the need for an encapsulation system for [...] Read more.
The success of mRNA vaccines against SARS-CoV-2 has prompted interest in mRNA-based pharmaceuticals due to their rapid production, adaptability, and safety. Despite these advantages, the inherent instability of mRNA and its rapid degradation in vivo underscores the need for an encapsulation system for the administration and delivery of RNA-based therapeutics. Lipid nanoparticles (LNPs) have proven the most robust and safest option for in vivo applications. However, the mid- to long-term storage of mRNA-LNPs still requires sub-zero temperatures along the entire chain of supply, highlighting the need to develop alternatives to improve mRNA vaccine stability under non-freezing conditions to facilitate logistics and distribution. Lyophilization presents itself as an effective alternative to prolong the shelf life of mRNA vaccines under refrigeration conditions, although a complex optimization of the process parameters is needed to maintain the integrity of the mRNA-LNPs. Recent studies have demonstrated the feasibility of freeze-drying LNPs, showing that lyophilized mRNA-LNPs retain activity and stability. However, long-term functional data remain limited. Herein, we focus on obtaining an optimized lyophilizable mRNA-LNP formulation through the careful selection of an optimal buffer and cryoprotectant and by tuning freeze-drying parameters. The results demonstrate that our optimized lyophilization process maintains LNP characteristics and functionality for over a year at refrigerated temperatures, offering a viable solution to the logistical hurdles of mRNA vaccine distribution. Full article
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<p>(<b>A</b>) Shelf temperature and vacuum setpoints used during the freeze-drying cycle. (<b>B</b>–<b>D</b>) Comparison of the physicochemical parameters of fresh (4 °C) and freeze-dried LNPs in two different buffers (PBS or Tris 5 mM) using sucrose or maltose as lyoprotectants. (<b>B</b>) Encapsulation efficiency of mRNA (%). (<b>C</b>) Particle size obtained by DLS and (<b>D</b>) the Z potential values. Dashed line indicates separation of the PBS and Tris groups. (<b>E</b>,<b>F</b>) Transfection efficiency of freeze-dried LNPs normalized with control LNPs in HeLa and 293T (<b>F</b>) cells. Vertical dashed line indicates separation of the PBS and Tris groups, horizontal dashed lines indicate the basal transfection efficiency respective to control non-lyophilized LNPs. Data are presented as the geometric mean of at least three independent replicates, and error bars indicate the standard deviation (±SD).</p>
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<p>(<b>A</b>) Shelf temperature and vacuum setpoints used during optimized freeze-drying. (<b>B</b>) Transfection efficiency of freeze-dried LNPs normalized with control samples at 4 °C in 293T cells. Horizontal dashed lines indicate the basal transfection efficiency respective to control non-lyophilized LNPs. (<b>C</b>,<b>D</b>) Comparison of the physicochemical parameters of fresh (4 °C) and freeze-dried LNPs obtained by the initial and modified methods, in 20% sucrose or 20% maltose. (<b>B</b>) Particle size obtained by DLS. (<b>C</b>) Encapsulation efficiency of mRNA (%). (<b>E</b>) Average luminescence radiance (p/s) and bioluminescence images of mice treated with mRNA-LNPs. Mice were intramuscularly injected with LNPs at a dose of 1 µg of LUC-encoding mRNA/animal, and bioluminescence images were taken four hours post inoculation using the IVIS Lumina XRMS Imaging System. For graphs (<b>B</b>–<b>D</b>), data are presented as the geometric mean of at least three independent replicates, and error bars indicate the standard deviation (±SD). For the graphs in (<b>E</b>), data are the geometric mean of independent duplicates (two mice injected per sample), and error bars indicate the standard deviation (±SD). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span>&lt; 0.005, ns = not significant.</p>
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<p>Physicochemical characterization of frozen (−80 °C) and freeze-dried LNPs stored at 4 °C, 25 °C, and 37 °C for up to 60 weeks. (<b>A</b>–<b>F</b>) Analysis of physicochemical properties: (<b>A</b>) particle size, (<b>B</b>) polydispersity index, (<b>C</b>) Z potential, (<b>D</b>) encapsulation efficiency (%) of mRNA, (<b>E</b>) total mRNA concentration obtained by RiboGreen assay, and (<b>F</b>) mRNA integrity (%) obtained by capillary electrophoresis. (<b>G</b>). Cryo-TEM images of liquid and lyophilized LNPs freshly prepared or stored at 4 °C, 25 °C, and 37 °C for 60 weeks. For each image, size distribution histograms with Gaussian fitting curves (solid lines) are depicted, obtained from the Cryo-TEM images (N = 150/image).</p>
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<p>Functional results of frozen (−80 °C) and freeze-dried LNPs stored at 4 °C, 25 °C, and 37 °C for up to 60 weeks. (<b>A</b>,<b>B</b>) Transfection efficiency of the indicated LNPs in HeLa (<b>A</b>) and 293T (<b>B</b>) cells. (<b>C</b>,<b>D</b>) Average luminescence radiance (<b>C</b>) and bioluminescence images (<b>D</b>) of mice treated with mRNA-LNPs. Mice were intramuscularly injected with LNPs at a dose of 1 µg of LUC-encoding mRNA/animal, and bioluminescence images were taken four hours post inoculation using the IVIS Lumina XRMS Imaging System (software version 4.8.2).</p>
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23 pages, 3901 KiB  
Article
Generation and Genetic Stability of a PolX and 5′ MGF-Deficient African Swine Fever Virus Mutant for Vaccine Development
by Daniel Pérez-Núñez, Daniel W. Madden, Gonzalo Vigara-Astillero, David A. Meekins, Chester D. McDowell, Bianca Libanori-Artiaga, Raquel García-Belmonte, Dashzeveg Bold, Jessie D. Trujillo, Konner Cool, Taeyong Kwon, Velmurugan Balaraman, Igor Morozov, Natasha N. Gaudreault, Yolanda Revilla and Juergen A. Richt
Vaccines 2024, 12(10), 1125; https://doi.org/10.3390/vaccines12101125 - 30 Sep 2024
Viewed by 453
Abstract
The African swine fever virus (ASFV) causes fatal disease in pigs and is currently spreading globally. Commercially safe vaccines are urgently required. Aiming to generate a novel live attenuated vaccine (LAV), a recombinant ASFV was generated by deleting the viral O174L (PolX) gene. [...] Read more.
The African swine fever virus (ASFV) causes fatal disease in pigs and is currently spreading globally. Commercially safe vaccines are urgently required. Aiming to generate a novel live attenuated vaccine (LAV), a recombinant ASFV was generated by deleting the viral O174L (PolX) gene. However, during in vitro generation, an additional spontaneous deletion of genes belonging to the multigene families (MGF) occurred, creating a mixture of two viruses, namely, Arm-ΔPolX and Arm-ΔPolX-ΔMGF. This mixture was used to inoculate pigs in a low and high dose to assess the viral dynamics of both populations in vivo. Although the Arm-ΔPolX population was a much lower proportion of the inoculum, in the high-dose immunized animals, it was the only resulting viral population, while Arm-ΔPolX-ΔMGF only appeared in low-dose immunized animals, revealing the role of deleted MGFs in ASFV fitness in vivo. Furthermore, animals in the low-dose group survived inoculation, whereas animals in the high-dose group died, suggesting that the lack of MGF and PolX genes, and not the PolX gene alone, led to attenuation. The two recombinant viruses were individually isolated and inoculated into piglets, confirming this hypothesis. However, immunization with the Arm-ΔPolX-ΔMGF virus did not induce protection against challenge with the virulent parental ASFV strain. This study demonstrates that deletion of the PolX gene alone neither leads to attenuation nor induces an increased mutation rate in vivo. Full article
(This article belongs to the Special Issue Immunization Strategies for Animal Health)
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<p>Generation of the recombinant Arm-∆PolX virus. (<b>A</b>) Schematic for the generation of Arm-∆PolX. Representation of the original wild-type Arm/07/CBM/c2 genome and the homologous recombination (HR) vector that contains the EGFP gene under the control of the p72 promoter and the flanking regions of the O174L gene (upper two images). The resulting recombinant Arm∆PolX virus is shown in the bottom genome schematic. (<b>B</b>) GFP-positive COS-1 cells were identified using fluorescence microscopy, indicating generation of the recombinant Arm-∆PolX virus. (<b>C</b>) Specific amplification of either GFP cassette (arrow) or O174L gene (arrowhead) from Arm-ΔPolX or pFL-O174L vector control, by PCR.</p>
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<p>Analysis of the 5′ end deletion in Arm-∆PolX-∆MGF. (<b>A</b>) Whole genome coverage plot using Illumina reads of the recombinant Arm-ΔPolX-ΔMGF virus mapped to the ASFV Arm/07/CBM/c2 genome. (<b>B</b>) Genomic map of Arm07 showing the putatively deleted region in the 5′ end of the genome. (<b>C</b>) PCR amplification of 1,260 bp amplicon confirming the 5′-end GAP. (<b>D</b>) Whole genome coverage plot using Illumina (above) or ONT (below) reads of the recombinant Arm-ΔPolX-ΔMGF virus mapped to the de novo assembled genome of Arm-ΔPolX-ΔMGF. (<b>E</b>) Pairwise alignment between the Arm/07/CBM/c2 parental genome and Arm-ΔPolX-ΔMGF using Snapgene Software (Version 3.2.1).</p>
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<p>(<b>A</b>) Genomic map of Arm/07/CBM/c2 showing the putatively deleted region in the 5′ end of the genome and the four MGF genes targeted for PCR and qPCR analysis (blue). (<b>B</b>) PCR amplification of the indicated genes from DNA of wild-type Arm/07/CBM/c2 (WT), Arm-∆PolX (∆), or negative control (C-). (<b>C</b>) DNA detection by qPCR of MGF110-1L, MGF360-1L, MGF360-4L, MGF360-9L, B602L, and O174L (PolX) genes in wild-type Arm/07/CBM/c2 and the recombinant virus stock. Significance is denoted as * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Clinical safety profile study of pigs inoculated with the mixed Arm-∆PolX-/Arm-∆PolX-∆MGF inoculum. (<b>A</b>) Average daily temperatures of pigs receiving a 10<sup>2</sup> (blue) or 10<sup>4</sup> (orange) pfu dose of the mixed Arm-∆PolX-/Arm-∆PolX-∆MGF inoculum, revealing an increase in average body temperature in pigs receiving the higher dose. (<b>B</b>) Survival analysis of piglets receiving a 10<sup>2</sup> (blue) or 10<sup>4</sup> (orange) pfu dose of the mixed Arm-∆PolX-/Arm-∆PolX-∆MGF inoculum. (<b>B</b>,<b>D</b>) Quantitative PCR (qPCR) was performed on pig blood to detect ASFV the B646L (p72) gene in pigs receiving the 10<sup>2</sup> pfu (<b>C</b>) or 10<sup>4</sup> pfu (<b>D</b>) dose of the Arm-∆PolX-/Arm-∆PolX-∆MGF mix. (<b>E</b>,<b>F</b>) Clinical scores of pigs receiving the 10<sup>2</sup> pfu (<b>E</b>) or 10<sup>4</sup> pfu (<b>F</b>) dose of the Arm-∆PolX-/Arm-∆PolX-∆MGF mix. Total scores were calculated based on several parameters, including fever (0–4), liveliness (0–3), body shape (0–3), respiratory function (0–3), neurological signs (0–3), skin lesions (0–3), ocular/nasal discharge (0–3), and digestive signs (0–3).</p>
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<p>Quantitative PCR (qPCR) to detect various ASFV genes. qPCR was performed to detect the B646L (p72), MGF360-1L, MGF110-1L, MGF360-4L, MGF360-9L, and O174L (PolX) genes. Samples tested are: (i) wild-type Arm/07/CBM/c2 and the mixed Arm-∆PolX/Arm-∆PolX-ΔMGF virus stock used in the in vivo studies; (ii) blood collected on 7 dpv from pigs receiving the 10<sup>2</sup> pfu dose of the virus stock (Study #1); and (iii) blood collected on 7 DPV (or 5 DPV; pig #2199) from pigs receiving the 10<sup>4</sup> pfu dose of the virus stock (Study #1). The relative amount of the B646L (p72) gene in relation to MGF genes provides insight into the relative amount of the single gene-deleted Arm-∆PolX virus versus the double-deleted Arm-∆PolX-∆MGF virus present in the respective pigs’ blood at the indicated time point.</p>
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<p>Overview of vaccination and challenge experiment. Piglets were divided into three groups of 6 animals each. Groups 1 and 2 were vaccinated IM with 10<sup>2</sup> HAD50 Arm-ΔPolX or Arm-ΔPolX-ΔMGF, with group 3 serving as unvaccinated controls. After 28 days, the surviving vaccinated animals and controls were challenged IM with 10<sup>2</sup> HAD50 Arm/07/CBM/c2. Necropsies were performed at the time of animal death or were scheduled for 14 days post-challenge for any animals surviving the challenge. Scheduled blood collections were performed on days listed in red.</p>
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<p>Arm-ΔPolX-ΔMGF is attenuated in vivo while Arm-ΔPolX retains its virulence. (<b>A</b>) Survival curves for Arm-ΔPolX and Arm-ΔPolX-ΔMGF vaccine groups. Curves were compared using the Mantel–Cox test. (<b>B</b>) Daily body temperatures for Arm-ΔPolX and Arm-ΔPolX-ΔMGF vaccine groups. (<b>C</b>) Daily clinical scores for Arm-ΔPolX and Arm-ΔPolX-ΔMGF vaccine groups. (<b>D</b>) qPCR values for Arm-ΔPolX and Arm-ΔPolX-ΔMGF groups from vaccination to challenge. Graphs were compared using multiple Mann–Whitney tests, and significant differences were noted at specific timepoints. For all panels, significance is denoted as * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Immunization with Arm-ΔPolX-ΔMGF did not confer protection against a homologous virulent challenge. (<b>A</b>) Survival curves for Arm07ΔPolX-ΔMFG vaccinated and unvaccinated animals. Curves were compared using the Mantel–Cox test. (<b>B</b>) Daily body temperatures for Arm-ΔPolX-ΔMGF and unvaccinated animals. (<b>C</b>) Daily clinical scores for Arm-ΔPolX-ΔMGF and unvaccinated animals. (<b>D</b>) qPCR values for Arm-ΔPolX-ΔMGF and unvaccinated animals. All graphs were compared using multiple Mann–Whitney tests but no significant differences at specific timepoints were found.</p>
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30 pages, 2349 KiB  
Article
On a Symmetry-Based Structural Deterministic Fractal Fractional Order Mathematical Model to Investigate Conjunctivitis Adenovirus Disease
by Mdi Begum Jeelani and Nadiyah Hussain Alharthi
Symmetry 2024, 16(10), 1284; https://doi.org/10.3390/sym16101284 - 30 Sep 2024
Viewed by 815
Abstract
In the last few years, the conjunctivitis adenovirus disease has been investigated by using the concept of mathematical models. Hence, researchers have presented some mathematical models of the mentioned disease by using classical and fractional order derivatives. A complementary method involves analyzing the [...] Read more.
In the last few years, the conjunctivitis adenovirus disease has been investigated by using the concept of mathematical models. Hence, researchers have presented some mathematical models of the mentioned disease by using classical and fractional order derivatives. A complementary method involves analyzing the system of fractal fractional order equations by considering the set of symmetries of its solutions. By characterizing structures that relate to the fundamental dynamics of biological systems, symmetries offer a potent notion for the creation of mechanistic models. This study investigates a novel mathematical model for conjunctivitis adenovirus disease. Conjunctivitis is an infection in the eye that is caused by adenovirus, also known as pink eye disease. Adenovirus is a common virus that affects the eye’s mucosa. Infectious conjunctivitis is most common eye disease on the planet, impacting individuals across all age groups and demographics. We have formulated a model to investigate the transmission of the aforesaid disease and the impact of vaccination on its dynamics. Also, using mathematical analysis, the percentage of a population which needs vaccination to prevent the spreading of the mentioned disease can be investigated. Fractal fractional derivatives have been widely used in the last few years to study different infectious disease models. Hence, being inspired by the importance of fractal fractional theory to investigate the mentioned human eye-related disease, we derived some adequate results for the above model, including equilibrium points, reproductive number, and sensitivity analysis. Furthermore, by utilizing fixed point theory and numerical techniques, adequate requirements were established for the existence theory, Ulam–Hyers stability, and approximate solutions. We used nonlinear functional analysis and fixed point theory for the qualitative theory. We have graphically simulated the outcomes for several fractal fractional order levels using the numerical method. Full article
(This article belongs to the Section Mathematics)
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<p>Schematic diagram of model (<a href="#FD3-symmetry-16-01284" class="html-disp-formula">3</a>).</p>
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<p>(<b>a</b>) Graphical presentation of <math display="inline"><semantics> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> </semantics></math> using <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>,</mo> <mspace width="4pt"/> <msub> <mi>μ</mi> <mi>n</mi> </msub> </mrow> </semantics></math>. (<b>b</b>) Graphical presentation of <math display="inline"><semantics> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> </semantics></math> using <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>,</mo> <mspace width="4pt"/> <mi>λ</mi> </mrow> </semantics></math>. (<b>c</b>) Graphical presentation of <math display="inline"><semantics> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> </semantics></math> using <math display="inline"><semantics> <mrow> <mi>γ</mi> <mo>,</mo> <mspace width="4pt"/> <mi>μ</mi> </mrow> </semantics></math>. (<b>d</b>) Graphical presentation of <math display="inline"><semantics> <msub> <mi mathvariant="script">R</mi> <mn>0</mn> </msub> </semantics></math> using <math display="inline"><semantics> <mrow> <mo>Λ</mo> <mo>,</mo> <mspace width="4pt"/> <mi>μ</mi> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of susceptible compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0.30</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of exposed compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0.30</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of vaccinated compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0.30</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of infected compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0.30</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of recovered compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>0.30</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of susceptible compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.40</mn> <mo>,</mo> <mn>0.60</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of exposed compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.40</mn> <mo>,</mo> <mn>0.60</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of vaccinated compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.40</mn> <mo>,</mo> <mn>0.60</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of infected compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.40</mn> <mo>,</mo> <mn>0.60</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of recovered compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.40</mn> <mo>,</mo> <mn>0.60</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of susceptible compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.60</mn> <mo>,</mo> <mn>0.80</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of exposed compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.60</mn> <mo>,</mo> <mn>0.80</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of vaccinated compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.60</mn> <mo>,</mo> <mn>0.80</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of infected compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.60</mn> <mo>,</mo> <mn>0.80</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of recovered compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.60</mn> <mo>,</mo> <mn>0.80</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of susceptible compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.80</mn> <mo>,</mo> <mn>1.00</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of exposed compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.80</mn> <mo>,</mo> <mn>1.00</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of vaccinated compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.80</mn> <mo>,</mo> <mn>1.00</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of infected compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.80</mn> <mo>,</mo> <mn>1.00</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of recovered compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.80</mn> <mo>,</mo> <mn>1.00</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of susceptible compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.90</mn> <mo>,</mo> <mn>0.98</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of exposed compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.90</mn> <mo>,</mo> <mn>0.98</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of vaccinated compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.90</mn> <mo>,</mo> <mn>0.98</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of infected compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.90</mn> <mo>,</mo> <mn>0.98</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>Dynamical behaviors of recovered compartments against using different fractal fractional orders in <math display="inline"><semantics> <mrow> <mo>(</mo> <mn>0.90</mn> <mo>,</mo> <mn>0.98</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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19 pages, 2943 KiB  
Review
Ferritin Vaccine Platform for Animal and Zoonotic Viruses
by Sohrab Ahmadivand, Robert Fux and Dušan Palić
Vaccines 2024, 12(10), 1112; https://doi.org/10.3390/vaccines12101112 - 27 Sep 2024
Viewed by 399
Abstract
Viral infections in animals continue to pose a significant challenge, affecting livestock health, welfare, and food safety, and, in the case of zoonotic viruses, threatening global public health. The control of viral diseases currently relies on conventional approaches such as inactivated or attenuated [...] Read more.
Viral infections in animals continue to pose a significant challenge, affecting livestock health, welfare, and food safety, and, in the case of zoonotic viruses, threatening global public health. The control of viral diseases currently relies on conventional approaches such as inactivated or attenuated vaccines produced via platforms with inherent limitations. Self-assembling ferritin nanocages represent a novel vaccine platform that has been utilized for several viruses, some of which are currently undergoing human clinical trials. Experimental evidence also supports the potential of this platform for developing commercial vaccines for veterinary viruses. In addition to improved stability and immunogenicity, ferritin-based vaccines are safe and DIVA-compatible, and can be rapidly deployed in response to emerging epidemics or pandemics. This review discusses the structural and functional properties of ferritin proteins, followed by an overview of the design and production of ferritin-based vaccines, the mechanisms of immune responses, and their applications in developing vaccines against animal and zoonotic viruses. Full article
(This article belongs to the Special Issue Vaccine Development for Emerging and Zoonotic Diseases)
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<p>Structure of ferritin nanocage. Schematic view of the ferritin nanocage with four (<b>A</b>) and two and three (<b>B</b>) axes of symmetry. (<b>C</b>) Ferritin monomer featuring the five α-helices (A, B, C, D, and short E α-helix). BC: Protein loop connecting B to C α-helix. Created with MOLSCRIPT [<a href="#B30-vaccines-12-01112" class="html-bibr">30</a>].</p>
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<p>Design Approaches for Antigen Presentation on Ferritin Platforms. (<b>A</b>) Genetic fusion: the gene encoding an antigen of interest is fused to the outer surface of the ferritin platform. (<b>B</b>) Chemical cross-linking: side chains on the antigen and the platform are connected by a cross-linker, e.g., SMPH [succinimidyl 6-(β-maleimidopropionamido) hexanoate]. Created with <a href="http://Biorender.com" target="_blank">Biorender.com</a> (accessed on 23 September 2024).</p>
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<p>SpyTag–SpyCatcher Design for Antigen Presentation on the Ferritin Platform. A SpyTag fused to the N-terminus of ferritin and the antigen–SpyCatcher fusion are expressed separately (<b>left panel</b>). Upon mixing, the SpyCatcher/SpyTag side chains form a covalent isopeptide bond with the release of water (<b>middle panel</b>), resulting in decorated vaccine nanoparticles via antigen display on the surface of the ferritin scaffold (<b>right panel</b>). Created with <a href="http://Biorender.com" target="_blank">Biorender.com</a> (accessed on 23 September 2024).</p>
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<p>Potential mechanisms of immune responses induced by ferritin-based vaccine NPs against infectious viral diseases. Ferritin-vaccine NPs are processed and presented by both MHC-I and MHC-II for recognition by CD8+ and CD4+ T cells with T cell receptors (TCRs). They enhance immunity mainly through direct B-cell activation and improved trafficking by APCs. The vaccine NPs can induce humoral immunity by directly activating B cells through cross-linking of BCRs and by MHC II-mediated activation of CD4+ T helper cells. T helper 2 (Th2) cells secrete cytokines like IL-4, whereas T follicular helper (Tfh) cells produce essential cytokines such as IL-21, which directly support B-cell activation and germinal center responses. This results in strong signaling that induces robust and durable antibody secretion by plasma cells and the generation of memory B cells. To elicit cellular immune responses, immature CD8+ T lymphocytes proliferate and differentiate into cytotoxic T cells (effector) and specific memory CTLs. CD4+ T helper cells (Th1) can also support activated CTLs by secreting cytokines (e.g., IFN- γ). Created with <a href="http://Biorender.com" target="_blank">Biorender.com</a> (accessed on 23 September 2024).</p>
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18 pages, 3148 KiB  
Article
Evaluating the Compatibility of Three Aluminum Salt-Adjuvanted Recombinant Protein Antigens (Trivalent NRRV) Combined with a Mock Trivalent Sabin-IPV Vaccine: Analytical and Formulation Challenges
by Prashant Kumar, Atsushi Hamana, Christopher Bird, Brandy Dotson, Soraia Saleh-Birdjandi, David B. Volkin and Sangeeta B. Joshi
Vaccines 2024, 12(10), 1102; https://doi.org/10.3390/vaccines12101102 - 26 Sep 2024
Viewed by 677
Abstract
In this work, we describe compatibility assessments of a recombinant, trivalent non-replicating rotavirus vaccine (t-NRRV) candidate with a mock trivalent Sabin inactivated polio vaccine (t-sIPV). Both t-sIPV and t-NRRV are incompatible with thimerosal (TH), a preservative commonly used in pediatric pentavalent combination vaccines [...] Read more.
In this work, we describe compatibility assessments of a recombinant, trivalent non-replicating rotavirus vaccine (t-NRRV) candidate with a mock trivalent Sabin inactivated polio vaccine (t-sIPV). Both t-sIPV and t-NRRV are incompatible with thimerosal (TH), a preservative commonly used in pediatric pentavalent combination vaccines (DTwP-Hib-HepB) distributed in low- and middle-income countries (LMICs), preventing the development of a heptavalent combination. The compatibility of t-NRRV with a mock DTwP-Hib-HepB formulation is described in a companion paper. This case study highlights the analytical and formulation challenges encountered when combining a mock t-sIPV vaccine (unadjuvanted) with Alhydrogel® (AH) adjuvanted t-NRRV. Selective and stability-indicating competition ELISAs were implemented to monitor antibody binding to each of the six antigens (±AH). Simple mixing caused the undesired desorption of t-NRRV from AH with the concomitant binding of t-sIPV to AH. Although the former effect was mitigated by dialyzing sIPV bulks, decreased sIPV storage stability was observed at accelerated temperatures in the bivalent combination with a rank-ordering of P[8] > P[6] > P[4] and sIPV3 > sIPV2 > sIPV1. The compatibility of AH-adsorbed t-sIPV with alternative preservatives was evaluated, and parabens (methyl, propyl) were identified for potential use in this multi-dose bivalent formulation. Along with a companion paper, the lessons learned are discussed to facilitate the future formulation development of pediatric combination vaccines with new antigens. Full article
(This article belongs to the Special Issue Recent Advances in Vaccine Adjuvants and Formulation)
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<p>Summary of the competitive ELISA method development results for aluminum salt-adjuvanted antigens (t-NRRV and t-sIPV) for combination in a bivalent combination vaccine. Schematic of the competitive ELISA assay format (panel (<b>A</b>)); this figure was published previously in our companion paper [<a href="#B9-vaccines-12-01102" class="html-bibr">9</a>] and is adapted here with permission from the <span class="html-italic">Vaccines</span> journal. The selectivity and stability indication results are shown for each antigen: NRRV P[4] (panels (<b>B</b>,<b>H</b>)), P[6] (panels (<b>C</b>,<b>I</b>)), P[8] (panels (<b>D</b>,<b>J</b>)), sIPV1 (panels (<b>E</b>,<b>K</b>)), sIPV2 (panels (<b>F</b>,<b>L</b>)), and sIPV3 (panels (<b>G</b>,<b>M</b>)). See the Methods Section for a description of samples and stress conditions. Data are presented as the mean ± range (n = 2). Panels (<b>H</b>–<b>J</b>) for the stability indication of NRRV antigens were previously published in our companion paper [<a href="#B9-vaccines-12-01102" class="html-bibr">9</a>] and are shown here with permission from the <span class="html-italic">Vaccines</span> journal.</p>
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<p>Compatibility of Alhydrogel™ (AH)-adjuvanted recombinant t-NRRV antigens (P[4], P[6], P[8]) with t-sIPV antigens (types 1, 2, and 3; no adjuvant) in a mock bivalent combination vaccine formulation (t-NRRV + t-sIPV). Panel (<b>A</b>) is schematic representation of the simple mixing together of AH-adsorbed t-NRRV antigens with t-sIPV (no adjuvant) antigens to prepare a bivalent vaccine formulation. Alum adsorption and antibody binding (relative antigen concentration normalized to 100% for each antigen) results are shown for (<b>B</b>,<b>C</b>) AH-adjuvanted t-NRRV, (<b>D</b>,<b>E</b>) a mock t-sIPV formulation, and (<b>F</b>,<b>G</b>) bivalent vaccine formulation. Panel (<b>H</b>) is schematic representation of a mitigation strategy (using sIPV bulks dialyzed in PBS to lower the phosphate buffer concentration) showing the simple mixing together of AH-adsorbed t-NRRV antigens with t-sIPV (no adjuvant) antigens to prepare a bivalent vaccine formulation. Alum adsorption and antibody binding results are shown for (<b>I</b>,<b>J</b>) AH-adjuvanted t-NRRV, (<b>K</b>,<b>L</b>) a mock t-sIPV formulation prepared using dialyzed viral bulks, and (<b>M</b>,<b>N</b>) bivalent vaccine formulation in PBS. Antibody binding and AH-adsorption for all antigens were measured by the competitive ELISA assays described in <a href="#vaccines-12-01102-f001" class="html-fig">Figure 1</a>. (*) no adsorption since AH is not present in the mock t-sIPV formulation. Red dashed line represents 100% adsorption, or 100% reference/relative antigen concentration. Data are presented as the mean ± range (n = 2) for AH adsorption and mean ± SD (n = 4) for antibody binding. When antigen adsorption to alum values was 100%, a range of ±10% was assigned based on the estimated LOQ of assay.</p>
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<p>Storage stability results for each NRRV antigen (P[4], P[6], and P[8]) in an AH-adsorbed t-NRRV formulation and when added to two different bivalent combination formulations with sIPV antigens as measured by competitive ELISAs. Percent antigen binding to antigen-specific mAb (relative to T = 0) in three formulations are shown for P[4] at 2–8 °C (<b>A</b>), 15 °C (<b>B</b>), 25 °C (<b>C</b>), and 37 °C (<b>D</b>); for P[6] at 2–8 °C (<b>E</b>), 15 °C (<b>F</b>), 25 °C (<b>G</b>), and 37 °C (<b>H</b>); and for P[8] at 2–8 °C (<b>I</b>), 15 °C (<b>J</b>), 25 °C (<b>K</b>), and 37 °C (<b>L</b>). Three formulations include t-NRRV (control of AH-adsorbed t-NRRV alone), bivalent formulation (AH-adjuvant t-NRRV mixed with t-sIPV bulks at higher phosphate concentrations), and low-phosphate bivalent formulation (AH-adjuvant with t-NRRV mixed with dialyzed t-sIPV bulk with a lower phosphate concentration). Data are presented as the mean ± SD (n = 4).</p>
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<p>Storage stability results for each sIPV antigen (types sIPV1, sIPV2, and sIPV3) in the t-sIPV formulation (AH-adsorbed) and when added to two different bivalent combination formulations with AH-adsorbed t-NRRV antigens as measured by Sabin D-antigen competitive ELISAs. Relative antigen binding to antigen-specific mAb (relative to T = 0) in three formulations are shown for sIPV1 at 2–8 °C (<b>A</b>), 15 °C (<b>B</b>), 25 °C (<b>C</b>), and 37 °C (<b>D</b>); for sIPV2 at 2–8 °C (<b>E</b>), 15 °C (<b>F</b>), 25 °C (<b>G</b>), and 37 °C (<b>H</b>); and for sIPV3 at 2–8 °C (<b>I</b>), 15 °C (<b>J</b>), 25 °C (<b>K</b>), and 37 °C (<b>L</b>). Three formulations include t-sIPV-adsorbed (control of AH-adsorbed t-sIPV alone), bivalent formulation (AH-adjuvant t-NRRV mixed with t-sIPV bulks at higher phosphate concentrations), and low-phosphate bivalent formulation (AH-adjuvant with t-NRRV mixed with dialyzed t-sIPV bulk with a lower phosphate concentration). Data are presented as the mean ± SD (n = 4).</p>
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<p>Stability profiles of AH-adsorbed t-sIPV antigens (types 1, 2, and 3) in the presence and absence of different antimicrobial preservatives (APs) as measured by Sabin D-antigen competitive ELISAs. The relative antigen–antibody binding of each AH-adsorbed sIPV antigen to an antigen-specific antibody is shown as a percentage concentration relative to time zero values after storage at 2–8 °C and 15 °C for 3 months, and 37 °C for 1 day, for sIPV1 (Panels (<b>A</b>,<b>D</b>,<b>G</b>)), sIPV2 (Panels (<b>B</b>,<b>E</b>,<b>H</b>)), and sIPV3 (Panels (<b>C</b>,<b>F</b>,<b>I</b>)) in the presence of the indicated AP. TH—thimerosal, 2-PE—2-phenoxy ethanol, PH—phenol, CB—chlorobutanol, MC—m-cresol, BA—benzyl alcohol, MP—methyl paraben, and PP—propyl paraben. Data are presented as the mean ± SD (n = 4).</p>
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12 pages, 1736 KiB  
Article
Evolutionary Insights from Association Rule Mining of Co-Occurring Mutations in Influenza Hemagglutinin and Neuraminidase
by Valentina Galeone, Carol Lee, Michael T. Monaghan, Denis C. Bauer and Laurence O. W. Wilson
Viruses 2024, 16(10), 1515; https://doi.org/10.3390/v16101515 - 25 Sep 2024
Viewed by 489
Abstract
Seasonal influenza viruses continuously evolve via antigenic drift. This leads to recurring epidemics, globally significant mortality rates, and the need for annually updated vaccines. Co-occurring mutations in hemagglutinin (HA) and neuraminidase (NA) are suggested to have synergistic interactions where mutations can increase the [...] Read more.
Seasonal influenza viruses continuously evolve via antigenic drift. This leads to recurring epidemics, globally significant mortality rates, and the need for annually updated vaccines. Co-occurring mutations in hemagglutinin (HA) and neuraminidase (NA) are suggested to have synergistic interactions where mutations can increase the chances of immune escape and viral fitness. Association rule mining was used to identify temporal relationships of co-occurring HA–NA mutations of influenza virus A/H3N2 and its role in antigenic evolution. A total of 64 clusters were found. These included well-known mutations responsible for antigenic drift, as well as previously undiscovered groups. A majority (41/64) were associated with known antigenic sites, and 38/64 involved mutations across both HA and NA. The emergence and disappearance of N-glycosylation sites in the pattern of N-X-[S/T] were also identified, which are crucial post-translational processes to maintain protein stability and functional balance (e.g., emergence of NA:339ASP and disappearance of HA:187ASP). Our study offers an alternative approach to the existing mutual-information and phylogenetic methods used to identify co-occurring mutations, enabling faster processing of large amounts of data. Our approach can facilitate the prediction of critical mutations given their occurrence in a previous season, facilitating vaccine development for the next flu season and leading to better preparation for future pandemics. Full article
(This article belongs to the Special Issue Virus Bioinformatics 2024)
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<p>Cluster of mutations in H3N2 during the transition from the (<b>A</b>) 2012/13 to 2013/14 and (<b>B</b>) 2014/15 to 2015/16 flu season. Transitions include HA and NA antigenic sites. Filled circles indicate mutations in hemagglutinin (yellow) and neuraminidase (blue), and an orange or dark blue border (e.g., node ha_D339N) indicates that the mutation occurred at an antigenic site, pink boxes indicates a (directed) rule identified by a number. The thickness of the edges represents the support of the rule and the direction show the antecedent and consequent.</p>
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<p>Maximum likelihood phylogenetic tree of 15 randomly selected HA sequences from each flu season with the amino acid illustrated for positions 144, 158, and 160 for each sequence. Coloured dots indicate the year isolated, showing highly temporal relationships between sequences and the protein position with the highest entropy.</p>
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28 pages, 3657 KiB  
Review
Development of mRNA Lipid Nanoparticles: Targeting and Therapeutic Aspects
by Yaping Liu, Yingying Huang, Guantao He, Chun Guo, Jinhua Dong and Linping Wu
Int. J. Mol. Sci. 2024, 25(18), 10166; https://doi.org/10.3390/ijms251810166 - 22 Sep 2024
Viewed by 3145
Abstract
Lipid nanoparticles (LNPs) have emerged as leading non-viral carriers for messenger RNA (mRNA) delivery in clinical applications. Overcoming challenges in safe and effective mRNA delivery to target tissues and cells, along with controlling release from the delivery vehicle, remains pivotal in mRNA-based therapies. [...] Read more.
Lipid nanoparticles (LNPs) have emerged as leading non-viral carriers for messenger RNA (mRNA) delivery in clinical applications. Overcoming challenges in safe and effective mRNA delivery to target tissues and cells, along with controlling release from the delivery vehicle, remains pivotal in mRNA-based therapies. This review elucidates the structure of LNPs, the mechanism for mRNA delivery, and the targeted delivery of LNPs to various cells and tissues, including leukocytes, T-cells, dendritic cells, Kupffer cells, hepatic endothelial cells, and hepatic and extrahepatic tissues. Here, we discuss the applications of mRNA–LNP vaccines for the prevention of infectious diseases and for the treatment of cancer and various genetic diseases. Although challenges remain in terms of delivery efficiency, specific tissue targeting, toxicity, and storage stability, mRNA–LNP technology holds extensive potential for the treatment of diseases. Full article
(This article belongs to the Special Issue Nanoparticles: From Synthesis to Applications 2.0)
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<p>Composition and models of mRNA lipid nanoparticles: (<b>a</b>) Chemical structure of representative lipids used for delivery of therapeutic nucleic acids. (<b>b</b>) Multilamellar vesicle (onion-like). (<b>c</b>) Particle with a nanostructured core. (<b>d</b>) Homogeneous core–shell structure. (<b>b</b>–<b>d</b>) are reprinted with permission from [<a href="#B15-ijms-25-10166" class="html-bibr">15</a>]. Copyright 2023 Elsevier Inc.</p>
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<p>The mRNA–LNP vaccine immune response process. Reprinted with permission from [<a href="#B35-ijms-25-10166" class="html-bibr">35</a>]. Copyright 2022 Elsevier Inc.</p>
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<p>Representative surface modification strategies for LNP targeting. Reprinted with permission from [<a href="#B39-ijms-25-10166" class="html-bibr">39</a>]. Copyright 2023 Wiley.</p>
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<p>Selective organ targeting (SORT) technology achieves liver- and lung-specific mRNA delivery. Reprinted with permission from [<a href="#B63-ijms-25-10166" class="html-bibr">63</a>]. Copyright 2023 Elsevier Inc.</p>
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<p>Key steps in the cancer immune cycle. Reprinted with permission from [<a href="#B123-ijms-25-10166" class="html-bibr">123</a>]. Copyright 2023 Elsevier Inc.</p>
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<p>Schematic illustration of the mRNA–LNP cancer vaccines. Reprinted with permission from [<a href="#B39-ijms-25-10166" class="html-bibr">39</a>]. Copyright 2023 Wiley.</p>
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<p>Representative administration routes and their applications of mRNA–LNP.</p>
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16 pages, 3500 KiB  
Article
Optimization of Cellular Transduction by the HIV-Based Pseudovirus Platform with Pan-Coronavirus Spike Proteins
by Syamala Rani Thimmiraju, Maria Jose Villar, Jason T. Kimata, Ulrich Strych, Maria Elena Bottazzi, Peter J. Hotez and Jeroen Pollet
Viruses 2024, 16(9), 1492; https://doi.org/10.3390/v16091492 - 20 Sep 2024
Viewed by 1173
Abstract
Over the past three years, new SARS-CoV-2 variants have continuously emerged, evolving to a point where an immune response against the original vaccine no longer provided optimal protection against these new strains. During this time, high-throughput neutralization assays based on pseudoviruses have become [...] Read more.
Over the past three years, new SARS-CoV-2 variants have continuously emerged, evolving to a point where an immune response against the original vaccine no longer provided optimal protection against these new strains. During this time, high-throughput neutralization assays based on pseudoviruses have become a valuable tool for assessing the efficacy of new vaccines, screening updated vaccine candidates against emerging variants, and testing the efficacy of new therapeutics such as monoclonal antibodies. Lentiviral vectors derived from HIV-1 are popular for developing pseudo and chimeric viruses due to their ease of use, stability, and long-term transgene expression. However, the HIV-based platform has lower transduction rates for pseudotyping coronavirus spike proteins than other pseudovirus platforms, necessitating more optimized methods. As the SARS-CoV-2 virus evolved, we produced over 18 variants of the spike protein for pseudotyping with an HIV-based vector, optimizing experimental parameters for their production and transduction. In this article, we present key parameters that were assessed to improve such technology, including (a) the timing and method of collection of pseudovirus supernatant; (b) the timing of host cell transduction; (c) cell culture media replenishment after pseudovirus adsorption; and (d) the centrifugation (spinoculation) parameters of the host cell+ pseudovirus mix, towards improved transduction. Additionally, we found that, for some pseudoviruses, the addition of a cationic polymer (polybrene) to the culture medium improved the transduction process. These findings were applicable across variant spike pseudoviruses that include not only SARS-CoV-2 variants, but also SARS, MERS, Alpha Coronavirus (NL-63), and bat-like coronaviruses. In summary, we present improvements in transduction efficiency, which can broaden the dynamic range of the pseudovirus titration and neutralization assays. Full article
(This article belongs to the Special Issue SARS-CoV-2 Neutralizing Antibodies 2.0)
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<p>Important steps (<b>A</b>–<b>H</b>) in pseudovirus production (<span class="html-italic">left</span>) and neutralization (<span class="html-italic">right</span>). (<b>A</b>) producer cell incubation (<span class="html-italic">left</span>)/sera heat inactivation (<span class="html-italic">right</span>) (<b>B</b>) co-transfection (<span class="html-italic">left</span>)/incubation (<span class="html-italic">right</span>) (<b>C</b>) pseudovirus supernatant collection (<span class="html-italic">left</span>)/neutralization (<span class="html-italic">right</span>) (<b>D</b>) Host cell incubation (<b>E</b>) Host cell Transduction (<b>F</b>) Spinoculation and incubation (<b>G</b>) Media replenishment (<b>H</b>) Luminescence reading. Within each panel, the left side depicts the original protocol, and the new protocol is shown on the right. In step B of the production panel, P = packaging plasmid, R = reporter plasmid, and S = spike envelop plasmid. The steps where experimental factors were optimized are indicated by green stars.</p>
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<p>(<b>A</b>) Timing of pseudovirus supernatant collection. The bar graph compares the timing of pseudovirus collection on transduction efficiency, reflected by luminescence (RLU). Data were analyzed for significance (<span class="html-italic">p</span> &lt; 0.05) using a two-tailed Mann–Whitney test. For all four variants studied, collecting pseudovirus supernatant at 48 h significantly (** represents <span class="html-italic">p</span> &lt; 0.01) enhanced transduction numbers over collection at 72 h. (<b>B</b>) Method of supernatant collection. For all four variants studied, unfiltered supernatant showed equal or better RLU numbers than filtered supernatant. Data were analyzed for significance (<span class="html-italic">p</span> &lt; 0.05) using a two-tailed Mann–Whitney test. RLU improvements were minimal for Delta (“ns” represents <span class="html-italic">p</span> &gt; 0.05), while they were moderate in the case of D614G and BA4 (** represent <span class="html-italic">p</span> &lt; 0.01), and maximum improvements (**** represent <span class="html-italic">p</span> &lt; 0.0001) were observed for the Beta variant.</p>
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<p>Host cell transduction timing after cell seeding (early vs. late transduction). Data were analyzed for significance (<span class="html-italic">p</span> &lt; 0.05) using a two-tailed Mann–Whitney test. Compared to late transduction (16–18 h), early transduction (4–6 h) significantly (** represent <span class="html-italic">p</span> &lt; 0.01) enhanced RLU numbers for all four variants studied. Data represents two different experiments with at least two replicates each.</p>
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<p>Replenishment of media. Changing the cell culture media (16–18 h) after pseudovirus transduction resulted in higher transduction of cells. Data were analyzed for significance (<span class="html-italic">p</span> &lt; 0.05) using a two-tailed Mann–Whitney test. Media change significantly (*** represent <span class="html-italic">p</span> &lt; 0.001) enhanced infectivity (transduction) numbers for both variants studied. The data represent two experiments with at least two replicates each.</p>
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<p>(<b>A</b>) Optimization of spinoculation speed and duration. The <span class="html-italic">X</span>-axis indicates a combination of centrifugation speeds (200 or 400× <span class="html-italic">g</span>) and duration (5, 30, or 60 min), while the <span class="html-italic">Y</span>-axis shows a % increase in RLU compared to no-spin controls. Data were analyzed for significance (<span class="html-italic">p</span> &lt; 0.05) using a two-tailed Mann–Whitney test. For both variants studied, spinning for 60 min enhanced infectivity numbers significantly (* represent <span class="html-italic">p</span> &lt; 0.05, ** represent <span class="html-italic">p</span> &lt; 0.01, *** represent <span class="html-italic">p</span> &lt; 0.001)); however, the spinoculation speed did not have a significant (“ns” represents <span class="html-italic">p</span> &gt; 0.05) impact. (<b>B</b>) Spinoculation enhanced RLU numbers significantly (* represent <span class="html-italic">p</span> &lt; 0.05, ** represent <span class="html-italic">p</span> &lt; 0.01). The <span class="html-italic">X</span>-axis indicates either the presence or the absence of the spinoculation treatment (200× <span class="html-italic">g</span> for 60 min), while the <span class="html-italic">Y</span>-axis shows RLU. Data were analyzed for significance (<span class="html-italic">p</span> &lt; 0.05) using a two-tailed Mann–Whitney test.</p>
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<p>Comparison of the original protocol with the new protocol for linearity in the titration of pseudovirus particles. The <span class="html-italic">X</span>-axis shows the dilution factor of the pseudovirus used for transducing host cells, while the <span class="html-italic">Y</span>-axis shows luminescence (RLU). For all four variants studied, the new protocol enhanced RLU numbers by at least one log value. Both protocols indicated good linearity (regression R-value range in the parenthesis −0.98 to −1.0) for all four variants. However, the linearity improved with the new protocol (5 points spanning a dilution of 8–128), compared to the original protocol (3 points spanning a dilution of 4–16) due to increased dynamic range.</p>
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<p>The effect of polybrene. RLU values (three different experiments with two replicates each) from the three protocols were analyzed for significance (<span class="html-italic">p</span> &lt; 0.05) using one-way ANOVA and the Tukey test for multiple comparisons (“ns” represent <span class="html-italic">p</span> &gt; 0.05, **** represent <span class="html-italic">p</span> &lt; 0.0001). The new protocol improved transduction numbers by at least a log-fold for all ten variants studied Polybrene in combination with the new protocol resulted in similar (Delta) or better (rest of the variants) transduction numbers.</p>
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<p>Coefficient of variation (CV%) analysis to assess variability within replicates in the original protocol, new protocol with polybrene, and new protocol without polybrene. CV% value is plotted on the <span class="html-italic">Y</span>-axis with each bar on the <span class="html-italic">X</span>-axis representing the value from 6 replicates of different spike variants. Variance observed using the original protocol ranged from 6 to 66%. With the new protocol (no polybrene), it ranged from 5 to 16%, and when polybrene was used in combination with the new protocol, the variance observed between the replicates was minimal at 4–13%.</p>
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19 pages, 4867 KiB  
Article
Vaccine Potency and Structure of Yeast-Produced Polio Type 2 Stabilized Virus-like Particles
by Qin Hong, Shuxia Wang, Xiaoli Wang, Wenyu Han, Tian Chen, Yan Liu, Fei Cheng, Song Qin, Shengtao Zhao, Qingwei Liu, Yao Cong and Zhong Huang
Vaccines 2024, 12(9), 1077; https://doi.org/10.3390/vaccines12091077 - 20 Sep 2024
Viewed by 798
Abstract
Poliovirus (PV) is on the brink of eradication due to global vaccination programs utilizing live-attenuated oral and inactivated polio vaccines. Recombinant PV virus-like particles (VLPs) are emerging as a safe next-generation vaccine candidate for the impending polio-free era. In this study, we investigate [...] Read more.
Poliovirus (PV) is on the brink of eradication due to global vaccination programs utilizing live-attenuated oral and inactivated polio vaccines. Recombinant PV virus-like particles (VLPs) are emerging as a safe next-generation vaccine candidate for the impending polio-free era. In this study, we investigate the production, antigenicity, thermostability, immunogenicity, and structures of VLPs derived from PV serotype 2 (PV2) wildtype strain and thermally stabilized mutant (wtVLP and sVLP, respectively). Both PV2 wtVLP and sVLP are efficiently produced in Pichia pastoris yeast. The PV2 sVLP displays higher levels of D-antigen and significantly enhanced thermostability than the wtVLP. Unlike the wtVLP, the sVLP elicits neutralizing antibodies in mice at levels comparable to those induced by inactivated polio vaccine. The addition of an aluminum hydroxide adjuvant to sVLP results in faster induction and a higher magnitude of neutralizing antibodies. Furthermore, our cryo-EM structural study of both sVLP and wtVLP reveals a native conformation for the sVLP and a non-native expanded conformation for the wtVLP. Our work not only validates the yeast-produced PV2 sVLP as a promising vaccine candidate with high production potential but also sheds light on the structural mechanisms that underpin the assembly and immunogenicity of the PV2 sVLP. These findings may expedite the development of sVLP-based PV vaccines. Full article
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<p>Expression of PV2 wtVLP and sVLP in <span class="html-italic">Pichia pastoris</span>. (<b>a</b>) Diagrams of the expression vectors. TRP2-L and TRP2-R, the up- and down-stream parts of the TRP region; P<sub>AOX1</sub>, AOX1 promoter; CYC1 TT, CYC1 transcription termination region; ADE2, expression cassette encoding phosphoribosylaminoimidazole carboxylase, used as the selection marker. (<b>b</b>) SDS-PAGE and Western blot analysis of the purified wtVLP and sVLP. The primary antibodies used in the Western blot assays are indicated. (<b>c</b>) Visualization of <span class="html-italic">P. pastoris</span>-derived VLPs by negative stain electron microscopy. Bar = 100 nm.</p>
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<p>Antigenicity of yeast-produced PV2 wtVLP and sVLP. (<b>a</b>) Reactivity of wtVLP and sVLP with anti-PV2 polyclonal antibody in ELISA. (<b>b</b>) Reactivity of wtVLP and sVLP with the D-antigen-specific mAb 1050 in ELISA. (<b>c</b>) D-antigen levels in the wtVLP or sVLP preparations. The data are presented as means ± SD from three independent measurements.</p>
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<p>Thermostability of PV2 wtVLP and sVLP. (<b>a</b>,<b>b</b>) Equal amounts of (<b>a</b>) wtVLP and (<b>b</b>) sVLP were subjected to incubation at different temperatures for 10 min and then analyzed by ELISA using the pAb or mAb1050. The red dash line and the dotted line indicate the mAb1050 reactivity (OD450 nm values) corresponding to 50% and 10% of the D-antigen in the untreated VLPs, respectively. Representative data from two independent experiments are shown. (<b>c</b>,<b>d</b>) Equal amounts of (<b>c</b>) wtVLP and (<b>d</b>) sVLP were subjected to incubation at 37 °C for different time periods as indicated and then analyzed by ELISA using pAb or mAb1050. The red dash line and the dotted line indicate the mAb1050 reactivity (OD450 nm values) corresponding to 50% and 10% of the D-antigen in the untreated VLPs, respectively. Representative data from two independent experiments are shown.</p>
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<p>Immunogenicity of PV2 wtVLP and sVLP in mice. (<b>a</b>) Mouse immunization and sampling schedule. (<b>b</b>) Neutralizing titers of the week-4 antisera against PV2 pseudovirus. (<b>c</b>) Neutralizing titers of the week-6 antisera against PV2 pseudovirus. Serum samples that exhibited less than 90% neutralization at the lowest serum dilution (1:16) were assigned a titer of 8 for computation of geometric means. Each symbol represents one mouse. The geometric mean titer for each group is shown. Statistical significance between two groups was calculated by Mann–Whitney two-tailed test. ns, no significant difference (<span class="html-italic">p</span> ≥ 0.05; *, <span class="html-italic">p</span> &lt; 0.05; **, <span class="html-italic">p</span> &lt; 0.01; ***, <span class="html-italic">p</span> &lt; 0.001; ****, <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Cryo-EM structures of the wtVLP and sVLP reveal that sVLP adopts a native conformation. (<b>a</b>,<b>b</b>) The overall cryo-EM density map of (<b>a</b>) wtVLP and (<b>b</b>) sVLP, viewed along the two-fold axis. The color bar indicates the corresponding radius from the center of the particle (units in Å). (<b>c</b>,<b>d</b>) The central section of the cryo-EM map of (<b>c</b>) wtVLP and (<b>d</b>) sVLP, illustrating the empty interior. (<b>e</b>) Model and map fitting of a single asymmetric unit of our cryo-EM maps. VP1, VP2, and VP3 are colored in blue, green, and red, respectively. (<b>f</b>) Zoom-in view of the VLPs around the 2-fold symmetry axis. (<b>g</b>) The density of the “pocket factor” in the sVLP pocket, modeled as sphingosine (in orange). (<b>h</b>,<b>i</b>) The “pocket” of the wtVLP map. The blue sVLP model with pocket factor colored in orange fitted in the wtVLP map shows the clash of pocket factor with Y159, F237 and N235 (<b>i</b>).</p>
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<p>Structural comparison and mutation site analysis of the VLPs. (<b>a</b>) Model of the asymmetric unit for sVLP, with mutations shown as yellow spheres. (<b>b</b>) A comparison of the asymmetric units of wtVLP (in hot pink) and sVLP (in colors). (<b>c</b>–<b>e</b>) Superposition of individual proteins: VP1 (<b>c</b>), VP2 (<b>d</b>), and VP3 (<b>e</b>) between wtVLP (in hot pink) and sVLP. (<b>f</b>) Surface representation of the amino acids surrounding Q178 (wtVLP)/L178 (sVLP), colored by hydrophobicity from dark cyan (hydrophilic) to dark gold (hydrophobic). The neighboring amino acids are labelled. (<b>g</b>) Measurements of the distances between surrounding amino acids of VP1 F134 (wtVLP),/L134 (sVLP) and Y159 (wtVLP)/F159 (sVLP). The wtVLP VP1 colored in hot pink and sVLP in blue. (<b>h</b>) The B-factor display of the two VLPs. The B-factor was calculated utilizing ChimeraX, with blue representing a low B-factor and red representing a high B-factor. (<b>i</b>) The calculated VPs interface within protomer.</p>
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21 pages, 4894 KiB  
Article
Development of a Two-Component Nanoparticle Vaccine Displaying an HIV-1 Envelope Glycoprotein that Elicits Tier 2 Neutralising Antibodies
by Kegomoditswe Malebo, Jeremy Woodward, Phindile Ximba, Qiniso Mkhize, Sanele Cingo, Thandeka Moyo-Gwete, Penny L. Moore, Anna-Lise Williamson and Rosamund Chapman
Vaccines 2024, 12(9), 1063; https://doi.org/10.3390/vaccines12091063 - 18 Sep 2024
Viewed by 835
Abstract
Despite treatment and other interventions, an effective prophylactic HIV vaccine is still an essential goal in the control of HIV. Inducing robust and long-lasting antibody responses is one of the main targets of an HIV vaccine. The delivery of HIV envelope glycoproteins (Env) [...] Read more.
Despite treatment and other interventions, an effective prophylactic HIV vaccine is still an essential goal in the control of HIV. Inducing robust and long-lasting antibody responses is one of the main targets of an HIV vaccine. The delivery of HIV envelope glycoproteins (Env) using nanoparticle (NP) platforms has been shown to elicit better immunogenicity than soluble HIV Env. In this paper, we describe the development of a nanoparticle-based vaccine decorated with HIV Env using the SpyCatcher/SpyTag system. The Env utilised in this study, CAP255, was derived from a transmitted founder virus isolated from a patient who developed broadly neutralising antibodies. Negative stain and cryo-electron microscopy analyses confirmed the assembly and stability of the mi3 into uniform icosahedral NPs surrounded by regularly spaced CAP255 gp140 Env trimers. A three-dimensional reconstruction of CAP255 gp140 SpyTag–SpyCatcher mi3 clearly showed Env trimers projecting from the centre of each of the pentagonal dodecahedral faces of the NP. To our knowledge, this is the first study to report the formation of SpyCatcher pentamers on the dodecahedral faces of mi3 NPs. To investigate the immunogenicity, rabbits were primed with two doses of DNA vaccines expressing the CAP255 gp150 and a mosaic subtype C Gag and boosted with three doses of the NP-developed autologous Tier 2 CAP255 neutralising antibodies (Nabs) and low levels of heterologous CAP256SU NAbs. Full article
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<p>Design and characterisation of HIV-1 CAP255 Env. (<b>A</b>) Schematic diagram of the original wildtype HIV-1 CAP255 gp160, the truncated CAP255 gp150 and the soluble CAP255 gp140 SpyTag. HIV-1 SP—signal peptide; REKR—furin cleavage site; TM—transmembrane region; TPA SP-tissue plasminogen activator leader sequence; FL-flexible linker; G501C—glycine-to-cysteine mutation at amino acid 501; T605C—threonine-to-cysteine mutation at amino acid 605; I559P—isoleucine-to-proline mutation at amino acid 559. (<b>B</b>) Western blot confirming expression of CAP255 gp140 SpyTag in the media of stably transfected HEK293 cells at passage 5 (P5) and 10 (P10). MW—molecular weight marker. (<b>C</b>) Graph showing the SEC profile of CAP255 gp140 SpyTag and the fractions analysed in (<b>D</b>) are shown. (<b>D</b>) Coomassie-stained Blue Native PAGE of CAP255 gp140 SpyTag purified by lectin affinity chromatography (LAC) and subsequent SEC fractions 36 to 46. CAP255 gp140 SpyTag trimers (***), dimers (**), monomers (*) and molecular weight in kDa (MW) are indicated.</p>
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<p>Western blotting confirming the expression of CAP255 gp150 Env and mosaic Gag.</p>
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<p>SpyTag–SpyCatcher coupling efficiency. (<b>A</b>) Coomassie-stained SDS-PAGE analysis of coupling reaction with MBP SpyTag**** and SpyCatcher mi3***** NPs at 1:1 and 1:2 molar ratios. Doubling the concentration of MBP SpyTag increased the amount of unbound MBP SpyTag****, and only a negligible amount of unbound SpyCatcher mi3***** remained. (<b>B</b>) Western blot (anti-His tag) analysis of CAP255 gp140 SpyTag:SpyCatcher mi3* coupling reaction. Doubling the concentration of CAP255 gp140 SpyTag had no noticeable effect on the amount of CAP255 gp140 SpyTag SpyCatcher mi3*, as unbound SpyCatcher mi3***** was still clearly visible. (<b>C</b>) Western blot (anti-Env) showing a large excess of CAP255 gp140 SpyTag** and an increase in CAP255 gp140 SpyTag SpyCatcher mi3* after doubling the concentration of CAP255 gp140 SpyTag** in the coupling reaction. (<b>D</b>) Diagrammatic representation of the proteins detected.</p>
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<p>NS-EM of the SpyCatcher mi3 NPs coupling. (<b>A</b>) SpyCatcher mi3 NPs and 2D class average (inset) showing successful assembly. (<b>B</b>) MBP SpyTag–SpyCatcher mi3 NPs; MBP densities make the SpyCatcher mi3 wall appear slightly thicker. (<b>C</b>) CAP255-gp 140 SpyTag–SpyCatcher mi3 NPs; excess Env particles can be seen in the background. Clear additional density corresponding to externally protruding Env trimers can be seen on the surface of the NP in the raw images and 2D class average (inset). The slight blurring of Env trimers, visible in the 2D class average, is likely due to conformational flexibility. (<b>D</b>) Three-dimensional map of SpyCatcher mi3 NP docked with the atomic coordinates of mi3 (PDB ID: 7B3Y in light blue), demonstrating good particle assembly. Additional density was observed at the pentagonal NP faces. (<b>E</b>,<b>F</b>) In negative stain, the details of this additional density could not be resolved, but its dimensions and shape correspond closely to five copies of SpyCatcher protein (PDB ID: 4MLS in dark blue). (<b>G</b>) CAP255 gp140 SpyTag–SpyCatcher mi3 NP reconstruction; the mi3 NP (PDB ID: 7B3Y in light blue) maintains rigidity and is surrounded by twelve regularly spaced Env trimers with high occupancy (36 copies of CAP255 gp140 SpyTag). The atomic structures of the Env trimer (PDB ID: 5JSA in yellow) correspond closely to the contours of the 3D map and are aligned along the five-fold axis. The clear 5-fold symmetry of the trimers is an artefact of imposing icosahedral symmetry on the trimer at the icosahedral 5-fold axes. (<b>H</b>,<b>I</b>) An additional disk of density corresponding to (<b>D</b>) was observed at the base of each Env trimer.</p>
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<p>Cryo-EM analysis of the assembled nanoparticles. (<b>A</b>) Two-dimensional class average of the CAP255 gp140 SpyTag SpyCatcher mi3 NP visualised along the icosahedral 2-fold symmetry axis. The stable mi3 core is surrounded by flexible CAP255 gp140 densities. (<b>B</b>) Density view of the icosahedral 3D reconstruction, rotated to the same orientation as (<b>A</b>). (<b>C</b>) Surface representation of the reconstruction; Env densities are centred on the 5-fold icosahedral symmetry axes. (<b>D</b>) Masked and symmetry-imposed mi3 NP density (blue) at 5.3 Å resolution, and CAP255 gp140 SpyTag–SpyCatcher density (yellow) at 17.7 Å resolution after focused classification without imposing symmetry. The atomic coordinates of mi3 (PDB ID: 7B3Y in light blue), SpyCatcher/SpyTag (PDB ID: 4MLS in dark blue and SpyTag in red) and the gp140 trimer (PDB ID: 5JSA yellow) are shown docked into the 3D reconstructions. (<b>E</b>,<b>F</b>) Five SpyCatcher monomers associate symmetrically about the five-fold axis to form a disk. The points of attachment to mi3 can be seen. Three-fold symmetric CAP255 gp140 SpyTag binds to the SpyCatcher disk. (<b>G</b>–<b>I</b>) The association between CAP255 gp140 SpyTag and SpyCatcher mi3 visualised along the icosahedral 5-fold symmetry axis shows the symmetry mismatch between the CAP255 gp140 SpyTag trimer and SpyCatcher pentamer. This results in a ratio of 3:2 (covalently linked SpyCatcher mi3: unlinked SpyCatcher mi3).</p>
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<p>Binding and neutralising antibody responses in rabbits. (<b>A</b>) Vaccination schedule and bleeds. (<b>B</b>) Binding antibody titres to CAP255 gp140 in sera of vaccinated rabbits. Where no binding was observed, the endpoint titre was plotted as 10. (<b>C</b>) Neutralising antibody titres in rabbit sera were measured using the pseudovirus-based TZM-bl neutralisation assay. The serum was taken 2 weeks after the second DNA prime (week 6), 2 weeks after the nanoparticle inoculation (week 14) and 4 weeks after the nanoparticle inoculation (week 16). Neutralisation titres were negative for all time points in the MuLV negative-control neutralisation assay. The 50% neutralisation titres (ID50) are colour-coded to reflect their potency range. Titres below 40 were considered non-neutralising and were not colour-coded.</p>
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<p>Neutralising antibody responses in rabbits. (<b>A</b>) Vaccination schedule and bleeds. (<b>B</b>) Neutralising antibody titres in rabbit sera were measured using the TZM-bl assay. The serum was taken 2 weeks after the second DNA prime (2nd DNA) and 2 weeks after each nanoparticle inoculation (1st NP, 2nd NP and 3rd NP). Neutralisation titres were negative for all time points in the MuLV negative-control neutralisation assay. The 50% neutralisation titres are colour-coded to reflect their potency range. Titres below 20 were considered non-neutralising and were not colour-coded.</p>
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18 pages, 3763 KiB  
Article
Molecular Dynamics Simulation of Lipid Nanoparticles Encapsulating mRNA
by Zhigang Zhang, Dazhi Cheng, Wenqin Luo, Donling Hu, Tiantian Yang, Kaixuan Hu, Li Liang, Wei Liu and Jianping Hu
Molecules 2024, 29(18), 4409; https://doi.org/10.3390/molecules29184409 - 17 Sep 2024
Viewed by 1415
Abstract
mRNA vaccines have shown great potential in responding to emerging infectious diseases, with their efficacy and stability largely dependent on the delivery vehicles—lipid nanoparticles (LNPs). This study aims to explore the mechanisms by which LNPs encapsulate mRNA, as well as the effects of [...] Read more.
mRNA vaccines have shown great potential in responding to emerging infectious diseases, with their efficacy and stability largely dependent on the delivery vehicles—lipid nanoparticles (LNPs). This study aims to explore the mechanisms by which LNPs encapsulate mRNA, as well as the effects of different N/P ratios and acid types in nucleic acid solutions on the structure and properties of LNPs, using the ethanol solvent injection method as the encapsulation technique. Six systems were designed, based on the composition and proportions of the existing mRNA vaccine mRNA-1273, and molecular dynamics (MD) simulations were employed to investigate the self-assembly process of LNPs. Ethanol was used as a solvent instead of pure water to better mimic experimental conditions. The results indicate that lipid components self-assemble into nanoparticles under neutral conditions, with the ionizable lipid SM-102 predominantly concentrating in the core of the particles. Upon mixing with nucleic acids in acidic conditions, LNPs undergo disassembly, during which protonated SM-102 encapsulates mRNA through electrostatic interactions, forming stable hydrogen bonds. Cluster structure analysis revealed that the four lipid components of LNPs are distributed sequentially from the outside inwards as DMG-PEG 2000, DSPC, cholesterol, and protonated SM-102. Moreover, LNPs constructed under low pH or low N/P ratios using citric acid exhibited larger volumes and more uniform distribution. These findings provide a scientific basis for further designing and optimizing LNP components to enhance the efficacy of mRNA vaccine encapsulation. Full article
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<p>Composition, mechanism of action, and preparation process of mRNA–LNPs. (<b>A</b>) Structure of ionizable cationic lipids. (<b>B</b>) Structure of cholesterol. (<b>C</b>) Structure of phospholipids. (<b>D</b>) Structure of polyethylene glycol (PEG). (<b>E</b>) Mechanism of action of mRNA vaccines in vivo. (<b>F</b>) Preparation process of mRNA–LNPs. mRNA primary sequence: TCGAACGTTCGAACGTTCGAACGTTCGAAT.</p>
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<p>Time evolution of RMSD (<b>A</b>) and potential energy (<b>B</b>) for the six systems.</p>
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<p>Representative MD simulation snapshots of the six systems: LNP_HAc (<b>A</b>), LNP (<b>B</b>), LNP_HAc_DNA (<b>C</b>), LNP_CA_DNA (<b>D</b>), LNP_HAc_2DNA (<b>E</b>), and LNP_CA_2DNA (<b>F</b>). The largest clusters at the 300 ns snapshot are highlighted with yellow dashed lines.</p>
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<p>Distribution of lipid components within the core clusters of the systems. (<b>A</b>) Average solvent-accessible surface area (SASA) percentage of lipids in the LNP_HAc and LNP systems. (<b>B</b>) Density distribution of the lipid core in the LNPs system.</p>
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<p>Radial distribution functions of nucleic acids and lipid components in the LNP_HAc_DNA, LNP_HAc_2DNA, LNP_CA_DNA, and LNP_CA_2DNA systems.</p>
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<p>Radial distribution functions of water molecules (<b>A</b>) and ethanol molecules (<b>B</b>) around nucleic acids in the LNP_HAc_DNA, LNP_HAc_2DNA, LNP_CA_DNA, and LNP_CA_2DNA systems.</p>
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<p>Formation and size distribution of clusters in the LNP_HAc_DNA, LNP_HAc_2DNA, LNP_CA_DNA, and LNP_CA_2DNA systems. (<b>A</b>) Variation in the number of clusters over time. (<b>B</b>) Number of lipid molecules in the core clusters over time. (<b>C</b>) Cluster size and number distribution within the systems at 300 ns.</p>
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<p>Binding free energy and energy decomposition of nucleic acids and lipids in the LNP_HAc_DNA, LNP_HAc_2DNA, LNP_CA_DNA, and LNP_CA_2DNA systems. (<b>A</b>) Decomposition of binding energy components for the four systems. <span class="html-italic">E</span><sub>VDW</sub> represents van der Waals binding energy under vacuum conditions, <span class="html-italic">E</span><sub>ELE</sub> represents electrostatic binding energy under vacuum conditions, <span class="html-italic">E</span><sub>GB</sub> represents polar solvation energy, and <span class="html-italic">E</span><sub>GBSUR</sub> represents non-polar solvation energy. (<b>B</b>) Further decomposition of the electrostatic energy for the four systems.</p>
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<p>Changes in solvent-accessible surface area (SASA) and hydrogen bond counts over time for nucleic acids and lipids in the LNP_HAc_DNA, LNP_HAc_2DNA, LNP_CA_DNA, and LNP_CA_2DNA systems. (<b>A</b>) Solvent-accessible surface area (SASA) as a function of time. (<b>B</b>) Total number of hydrogen bonds within the system over time. (<b>C</b>) Number of hydrogen bonds between lipid components and nucleic acids over time. (<b>D</b>) Three-dimensional visualization of hydrogen bonds between nucleic acids and ionizable lipids, with blue and red spheres representing nitrogen and oxygen atoms of the ionizable lipids, respectively.</p>
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