A comparative computational genomics of Ebola Virus Disease strains: In-silico Insight for Ebola control

Oluwagbemi, Olugbenga; Awe, Olaitan I.

doi:10.1016/j.imu.2018.07.004

Cited by 17 publications

(14 citation statements)

References 133 publications

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“…To the best of our knowledge, this study is the first to highlight and predict susceptibility and infection spread among key commodity actors (individuals) during bats migration season in some affected countries. This study identified hunting, trading, and consumption of bats as major triggers of the transmission of EVD from bats to people in affected countries, and the influence of bats migratory season as a probabilistic contributor to new EVD transmissions [ 22]. This call for governments and relevant policy-makers to strategically implement policies to protect their citizens from contracting EVD.…”

Section: Discussionmentioning

confidence: 99%

“…However, limited studies have been conducted on fruit bats migratory behavior and seasonality in the face of escalating environmental degradation and climatic variations [19][20][21]. Using a historical data with respect to EVD outbreaks in 1977and 1979 in Sudan;1994, 1996-1997in Gabon, 2001-2003 in the Republic of Congo as neighboring countries [22] to model and predict outbreaks of EVD will be a useful tool for both researchers and stakeholders in the fight against EVD epidemic across these across countries.…”

Section: Research Conducted In the Republic Of Congo (Rc) And Gabon Bmentioning

confidence: 99%

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Prediction of Ebola Virus Disease (EVD) infestation trends across fruit bats migratory seasons

Namahoro¹,

Haijun²

2019

Preprint

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Background : The majority of the rural populace of West and Central Africa participate actively in bushmeat commodity trade. The peak of this trade usually occurs during the migration of massive flocks of fruit bats. Unfortunately, this commercial period happens to coincides with the most conducive time for the spread of Ebola Virus Disease (EVD) which is transmitted by these fruit bats. We, therefore, conducted this study to predict EVD infestation trend among individuals who might be vulnerable and/or infected (hunters, vendors, and consumers of bushmeat) as a result of this seasonal bats migration. Methods: A secondary data collected in Ghana and semi-structured questionnaires were administered to international students from 7 countries (Democratic Republic of Congo (DRC), Ghana, Liberia, Zambia, Gabon, Guinea, and Ivory-Coast) and responses collated were used in this study. Bivariate correlation, probabilistic transmission rate of EVD, and probabilistic model (P-SIR) were employed to predict susceptible and infected individuals. Results : In all affected countries, it was observed that; there was a yearly bats’ migration pattern. 80 % of all interviewees confirmed that hunting, trading and consuming of bats influenced the rapid spread of EVD. The bivariate correlation between fruit bats and people involved in the bushmeat commodity chain was significant (p < 0.05) and the probability of infected bats were equally significant across the sampled countries. The estimated probabilistic rate was between 0.0099 and 0.01120, while individuals susceptible to EVD were predicted to be approximately between 5 and 45 (hunters), 0-18 f(vendors), and 25-90 (consumers). Conclusion : The migratory season of fruit bats contributes to EVD transmission in the most affected countries. People should avoid bushmeat consumption or eradicate all activities leading to the interaction with fruit bats to reduce the spread of EVD (Ebola) especially during the migratory seasons. Policy-makers and government agencies should also put in mechanisms that will protect their citizens during the fruit bats season. Keywords : bushmeat commodity chain, Ebola, migration season, EVD, fruit bats

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Section: Discussionmentioning

confidence: 99%

Section: Research Conducted In the Republic Of Congo (Rc) And Gabon Bmentioning

confidence: 99%

Prediction of Ebola Virus Disease (EVD) infestation trends across fruit bats migratory seasons

Namahoro¹,

Haijun²

2019

Preprint

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“…This was a retrospective descriptive study. The source organism in our analysis was the Bundibugyo Ebolavirus strain, which has a genome size of 18,940 base pairs (32). We used publicly available sequence data that was retrieved from the National Center for Biotechnology and Information Genbank database (33) The NCBI, has the Sequence Read Archive (SRA) repository and the Nucleotide sequence database (34).…”

Section: Methodsmentioning

confidence: 99%

“…In order to strengthen the response preparedness of the Viral Hemorrhagic Fever (VHF) in Uganda, the UVRI re-initiated the VHF National Surveillance programme in the year 2010 [34]. This was through an agreement between the Uganda Ministry of Health, Uganda Virus Research Institute and the US Centers for Disease Control and Prevention [32]. To date, UVRI serves as the national and regional reference laboratory for detection and response to VHF outbreaks which are of public health relevance in the region [35].…”

Section: Introductionmentioning

confidence: 99%

Genetic Diversity of Bundibugyo Ebolavirus from Uganda and the Democratic Republic of Congo

Omara

Muyingo

Balinandi

et al. 2021

Preprint

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Background The Ebolavirus is one of the deadliest viral pathogens which was first discovered in the year 1976 during two consecutive outbreaks in the Democratic Republic of Congo and Sudan. Six known strains have been documented. The Bundibugyo Ebolavirus in particular first emerged in the year 2007 in Uganda. This outbreak was constituted with 116 human cases and 39 laboratory confirmed deaths. After 5 years, it re-emerged and caused an epidemic for the first time in the Democratic Republic of Congo in the year 2012 as reported by the WHO. Here , 36 human cases with 13 laboratory confirmed deaths were registered. Despite several research studies conducted in the past, there is still scarcity of knowledge available on the genetic diversity of Bundibugyo Ebolavirus . We undertook a research project to provide insights into the unique variants of Bundibugyo Ebolavirus that circulated in the two epidemics that occurred in Uganda and the Democratic Republic of Congo Materials and Methods The Bioinformatics approaches used were; Quality Control, Reference Mapping, Variant Calling, Annotation, Multiple Sequence Alignment and Phylogenetic analysis to identify genomic variants as well determine the genetic relatedness between the two epidemics. Overall, we used 41 viral sequences that were retrieved from the publicly available sequence database, which is the National Center for Biotechnology and Information Gen-bank database. Results Our analysis identified 14,362 unique genomic variants from the two epidemics. The Uganda isolates had 5,740 unique variants, 75 of which had high impacts on the genomes. These were 51 frameshift, 15 stop gained, 5 stop lost, 2 missense, 1 synonymous and 1 stop lost and splice region. Their effects mainly occurred within the L-gene region at reference positions 17705, 11952, 11930 and 11027. For the DRC genomes, 8,622 variant sites were identified. The variants had a modifier effect on the genome occurring at reference positions, 213, 266 and 439. Examples are C213T, A266G and C439T. Phylogenetic reconstruction identified two separate and unique clusters from the two epidemics. Conclusion Our analysis provided further insights into the genetic diversity of Bundibugyo Ebolavirus from the two epidemics. The Bundibugyo Ebolavirus strain was genetically diverse with multiple variants. Phylogenetic reconstruction identified two unique variants. This signified an independent spillover event from a natural reservoir, rather a continuation from the ancestral outbreak that initiated the resurgence in the year 2012. Therefore, the two epidemics were not genetically related.

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“…Many scientific literatures have adopted the knowledge of computational genomics and immunoinformatic to proffer solutions to different diseases [ [85] , [86] , [87] ]. Many pieces of scientific literature have been published on the computational design of glycoprotein multi-epitope subunit vaccine candidates [ [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] ], but none has been specifically focused yet on the computational design of vaccine candidate for the old and new variants of the entire South African SARS-CoV-2 virus data strains from the year 2020–2021 [ [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] ].…”

Section: Introductionmentioning

confidence: 99%

Computational construction of a glycoprotein multi-epitope subunit vaccine candidate for old and new South-African SARS-CoV-2 virus strains

Oluwagbemi

Oladipo

Dairo

et al. 2022

Informatics in Medicine Unlocked

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The discovery of a new SARS-CoV-2 virus strain in South Africa presents a major public health threat, therefore contributing to increased infections and transmission rates during the second wave of the global pandemic. This study lays the groundwork for the development of a novel subunit vaccine candidate from the circulating strains of South African SARS-CoV-2 and provides an understanding of the molecular epidemiological trend of the circulating strains. A total of 475 whole-genome nucleotide sequences from South Africa submitted between December 1, 2020 and February 15, 2021 available at the GISAID database were retrieved based on its size, coverage level and hosts. To obtain the distribution of the clades and lineages of South African SARS-CoV-2 circulating strains, the metadata of the sequence retrieved were subjected to an epidemiological analysis. There was a prediction of the cytotoxic T lymphocytes (CTL), Helper T cells (HTL) and B-cell epitopes. Furthermore, there was allergenicity, antigenicity and toxicity predictions on the epitopes. The analysis of the physicochemical properties of the vaccine construct was performed; the secondary structure, tertiary structure and B-cell 3D conformational structure of the vaccine construct were predicted. Also, molecular binding simulations and dynamics simulations were adopted in the prediction of the vaccine construct's stability and binding affinity with TLRs. Result obtained from the metadata analysis indicated lineage B.1.351 to be in higher circulation among various circulating strains of SARS-CoV-2 in South Africa and GH has the highest number of circulating clades. The construct of the novel vaccine was antigenic, non-allergenic and non-toxic. The Instability index (II) score and aliphatic index were estimated as 41.74 and 78.72 respectively. The computed half-life in mammalian reticulocytes was 4.4 h in vitro, for yeast and in E. coli was >20 h and >10 h in vivo respectively. The grand average of hydropathicity (GRAVY) score is estimated to be −0.129, signifying the hydrophilic nature of the protein. The molecular docking indicates that the vaccine construct has a high binding affinity towards the TLRs with TLR 3 having the highest binding energy (−1203.2 kcal/mol) and TLR 9 with the lowest (−1559.5 kcal/mol). These results show that the vaccine construct is promising and should be evaluated using animal model.

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A comparative computational genomics of Ebola Virus Disease strains: In-silico Insight for Ebola control

Cited by 17 publications

References 133 publications

Prediction of Ebola Virus Disease (EVD) infestation trends across fruit bats migratory seasons

Prediction of Ebola Virus Disease (EVD) infestation trends across fruit bats migratory seasons

Genetic Diversity of Bundibugyo Ebolavirus from Uganda and the Democratic Republic of Congo

Computational construction of a glycoprotein multi-epitope subunit vaccine candidate for old and new South-African SARS-CoV-2 virus strains

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