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Omar KM, Kitundu GL, Jimoh AO, Namikelwa DN, Lisso FM, Babajide AA, Olufemi SE, Awe OI. Investigating antimicrobial resistance genes in Kenya, Uganda and Tanzania cattle using metagenomics. PeerJ 2024; 12:e17181. [PMID: 38666081 PMCID: PMC11044882 DOI: 10.7717/peerj.17181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/11/2024] [Indexed: 04/28/2024] Open
Abstract
Antimicrobial resistance (AMR) is a growing problem in African cattle production systems, posing a threat to human and animal health and the associated economic value chain. However, there is a poor understanding of the resistomes in small-holder cattle breeds in East African countries. This study aims to examine the distribution of antimicrobial resistance genes (ARGs) in Kenya, Tanzania, and Uganda cattle using a metagenomics approach. We used the SqueezeMeta-Abricate (assembly-based) pipeline to detect ARGs and benchmarked this approach using the Centifuge-AMRplusplus (read-based) pipeline to evaluate its efficiency. Our findings reveal a significant number of ARGs of critical medical and economic importance in all three countries, including resistance to drugs of last resort such as carbapenems, suggesting the presence of highly virulent and antibiotic-resistant bacterial pathogens (ESKAPE) circulating in East Africa. Shared ARGs such as aph(6)-id (aminoglycoside phosphotransferase), tet (tetracycline resistance gene), sul2 (sulfonamide resistance gene) and cfxA_gen (betalactamase gene) were detected. Assembly-based methods revealed fewer ARGs compared to read-based methods, indicating the sensitivity and specificity of read-based methods in resistome characterization. Our findings call for further surveillance to estimate the intensity of the antibiotic resistance problem and wider resistome classification. Effective management of livestock and antibiotic consumption is crucial in minimizing antimicrobial resistance and maximizing productivity, making these findings relevant to stakeholders, agriculturists, and veterinarians in East Africa and Africa at large.
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Affiliation(s)
- Kauthar M. Omar
- Department of Biochemistry and Biotechnology, School of Pure and Applied Sciences, Pwani University, Kilifi, Kenya
| | - George L. Kitundu
- Department of Biochemistry and Biotechnology, School of Pure and Applied Sciences, Pwani University, Kilifi, Kenya
| | - Adijat O. Jimoh
- Division of Immunology, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
- Genetics, Genomics and Bioinformatics Department, National Biotechnology Development Agency, Abuja, Nigeria
| | - Dorcus N. Namikelwa
- Department of Data Management, Modelling and Geo-Information Unit, International Centre of Insect Physiology and Ecology, Nairobi, Kenya
| | - Felix M. Lisso
- Department of Biochemistry and Biotechnology, School of Pure and Applied Sciences, Pwani University, Kilifi, Kenya
| | - Abiola A. Babajide
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Seun E. Olufemi
- Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
| | - Olaitan I. Awe
- African Society for Bioinformatics and Computational Biology, Cape Town, South Africa
- Department of Computer Science, University of Ibadan, Ibadan, Oyo State, Nigeria
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Bioinformatics, Computational Informatics, and Modeling Approaches to the Design of mRNA COVID-19 Vaccine Candidates. COMPUTATION 2022. [DOI: 10.3390/computation10070117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
This article is devoted to applying bioinformatics and immunoinformatics approaches for the development of a multi-epitope mRNA vaccine against the spike glycoproteins of circulating SARS-CoV-2 variants in selected African countries. The study’s relevance is dictated by the fact that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began its global threat at the end of 2019 and since then has had a devastating impact on the whole world. Measures to reduce threats from the pandemic include social restrictions, restrictions on international travel, and vaccine development. In most cases, vaccine development depends on the spike glycoprotein, which serves as a medium for its entry into host cells. Although several variants of SARS-CoV-2 have emerged from mutations crossing continental boundaries, about 6000 delta variants have been reported along the coast of more than 20 countries in Africa, with South Africa accounting for the highest percentage. This also applies to the omicron variant of the SARS-CoV-2 virus in South Africa. The authors suggest that bioinformatics and immunoinformatics approaches be used to develop a multi-epitope mRNA vaccine against the spike glycoproteins of circulating SARS-CoV-2 variants in selected African countries. Various immunoinformatics tools have been used to predict T- and B-lymphocyte epitopes. The epitopes were further subjected to multiple evaluations to select epitopes that could elicit a sustained immunological response. The candidate vaccine consisted of seven epitopes, a highly immunogenic adjuvant, an MHC I-targeting domain (MITD), a signal peptide, and linkers. The molecular weight (MW) was predicted to be 223.1 kDa, well above the acceptable threshold of 110 kDa on an excellent vaccine candidate. In addition, the results showed that the candidate vaccine was antigenic, non-allergenic, non-toxic, thermostable, and hydrophilic. The vaccine candidate has good population coverage, with the highest range in East Africa (80.44%) followed by South Africa (77.23%). West Africa and North Africa have 76.65% and 76.13%, respectively, while Central Africa (75.64%) has minimal coverage. Among seven epitopes, no mutations were observed in 100 randomly selected SARS-CoV-2 spike glycoproteins in the study area. Evaluation of the secondary structure of the vaccine constructs revealed a stabilized structure showing 36.44% alpha-helices, 20.45% drawn filaments, and 33.38% random helices. Molecular docking of the TLR4 vaccine showed that the simulated vaccine has a high binding affinity for TLR-4, reflecting its ability to stimulate the innate and adaptive immune response.
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Oluwagbemi OO, Oladipo EK, Dairo EO, Ayeni AE, Irewolede BA, Jimah EM, Oyewole MP, Olawale BM, Adegoke HM, Ogunleye AJ. Computational construction of a glycoprotein multi-epitope subunit vaccine candidate for old and new South-African SARS-CoV-2 virus strains. INFORMATICS IN MEDICINE UNLOCKED 2022; 28:100845. [PMID: 35071728 PMCID: PMC8760845 DOI: 10.1016/j.imu.2022.100845] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/28/2021] [Accepted: 01/01/2022] [Indexed: 12/19/2022] Open
Abstract
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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Affiliation(s)
- Olugbenga Oluseun Oluwagbemi
- Department of Computer Science and Information Technology, Sol Plaatje University, 8301, Kimberley, South Africa
- Department of Mathematical Sciences, Stellenbosch University, 7602, Matieland, South Africa
- National Institute of Theoretical and Computational Sciences (NiTheCS), South Africa
| | - Elijah Kolawole Oladipo
- Department of Microbiology, Laboratory of Molecular Biology, Immunology and Bioinformatics, Adeleke University, Ede, Osun State, Nigeria
- Genomics Unit, Helix Biogen Institute, Ogbomoso, Oyo State, Nigeria
| | - Emmanuel Oluwatobi Dairo
- Department of Virology, College of Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria
- Genomics Unit, Helix Biogen Institute, Ogbomoso, Oyo State, Nigeria
| | - Ayodele Eugene Ayeni
- Genomics Unit, Helix Biogen Institute, Ogbomoso, Oyo State, Nigeria
- Department of Medical Microbiology and Parasitology, Faculty of Basic Medical Sciences, University of Ibadan, Ibadan, Oyo State, Nigeria
| | | | - Esther Moradeyo Jimah
- Department of Medical Microbiology and Parasitology, University of Ilorin, Kwara State, Nigeria
- Genomics Unit, Helix Biogen Institute, Ogbomoso, Oyo State, Nigeria
| | - Moyosoluwa Precious Oyewole
- Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
- Genomics Unit, Helix Biogen Institute, Ogbomoso, Oyo State, Nigeria
| | - Boluwatife Mary Olawale
- Reproduction and Bioinformatics Unit, Department of Physiology, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
- Genomics Unit, Helix Biogen Institute, Ogbomoso, Oyo State, Nigeria
| | | | - Adewale Joseph Ogunleye
- Moscow Institute of Physics and Technology, 141701, Dolgoprudny, Moscow Oblast, Russian Federation
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