Comparative Genomic Analysis of Antimicrobial Resistance (AMR) Genes in Streptococcus agalactiae Reveals Gaps Limiting Artificial Intelligence (AI)-Driven Surveillance in Africa
1
Department of Research & Development, Genomac Institute Inc., Ogbomoso, Oyo State, Nigeria
2
Department of Biomedical Engineering, University of Ilorin, Ilorin, Kwara State, Nigeria
3
Department of Microbiology, Abdul Wali Khan University, Mardan, Pakistan, Pakistan
Popul. Med. 2026;8(Supplement Supplement 1):
ABSTRACT
BACKGROUND:
Streptococcus agalactiae (S. agalactiae) is a leading cause of neonatal sepsis and stillbirth globally. Although Africa bears a high burden, its contribution to genomic surveillance remains limited, constraining timely antimicrobial resistance (AMR) detection and the application of artificial intelligence (AI) enabled surveillance tools. AI-driven AMR surveillance relies on robust and updated genomic datasets. Sparse data reduce the reliability of AI models in detecting AMR trends. This study compared the genomic AMR landscape of S. agalactiae clinical isolates from selected African and developed countries and highlighted gaps limiting AI-supported AMR monitoring in Africa.
METHODS:
A cross-sectional bioinformatics analysis was conducted using publicly available S. agalactiae genomes from National Centre for Biotechnology Information (NCBI) Pathogen Detection Portal. Clinical isolates collected between 2015 and 2019 from seven countries (Botswana, Ethiopia, Mozambique, South Africa, Singapore, the United Kingdom, and the United States) were included. Only human isolates were analysed. AMR genes were identified using ResFinder database. A total of 35 genomes (five per country) were examined.
RESULTS:
The macrolide resistance gene mre(A), conferring resistance to macrolides such as erythromycin and azithromycin, dominated African isolates (100% in Botswana and Mozambique; 80% in Ethiopia and South Africa). In contrast, erm(B) and erm(A), linked to resistance against macrolides and lincosamides (e.g., clindamycin) were more frequent in isolates from Singapore and the United States. Only seven African countries had publicly available genomes, with many lacking representation. Post–COVID-19 genomic submissions from Africa declined sharply, highlighting weakened sequencing activity and fragmented data pipelines (critical limitations for AI-driven surveillance).
CONCLUSIONS:
Despite a high disease burden, Africa’s limited genomic representation constrains AI-enabled AMR detection and prediction. A novel idea of “SAGA-Africa (Streptococcus agalactiae Genomic Alert-Africa)” was proposed, which calls for regional genomic capacity strengthening, mandatory cross-border sequence virtual sharing, alongside installing mobile genomic units in endemic zones.
Streptococcus agalactiae (S. agalactiae) is a leading cause of neonatal sepsis and stillbirth globally. Although Africa bears a high burden, its contribution to genomic surveillance remains limited, constraining timely antimicrobial resistance (AMR) detection and the application of artificial intelligence (AI) enabled surveillance tools. AI-driven AMR surveillance relies on robust and updated genomic datasets. Sparse data reduce the reliability of AI models in detecting AMR trends. This study compared the genomic AMR landscape of S. agalactiae clinical isolates from selected African and developed countries and highlighted gaps limiting AI-supported AMR monitoring in Africa.
METHODS:
A cross-sectional bioinformatics analysis was conducted using publicly available S. agalactiae genomes from National Centre for Biotechnology Information (NCBI) Pathogen Detection Portal. Clinical isolates collected between 2015 and 2019 from seven countries (Botswana, Ethiopia, Mozambique, South Africa, Singapore, the United Kingdom, and the United States) were included. Only human isolates were analysed. AMR genes were identified using ResFinder database. A total of 35 genomes (five per country) were examined.
RESULTS:
The macrolide resistance gene mre(A), conferring resistance to macrolides such as erythromycin and azithromycin, dominated African isolates (100% in Botswana and Mozambique; 80% in Ethiopia and South Africa). In contrast, erm(B) and erm(A), linked to resistance against macrolides and lincosamides (e.g., clindamycin) were more frequent in isolates from Singapore and the United States. Only seven African countries had publicly available genomes, with many lacking representation. Post–COVID-19 genomic submissions from Africa declined sharply, highlighting weakened sequencing activity and fragmented data pipelines (critical limitations for AI-driven surveillance).
CONCLUSIONS:
Despite a high disease burden, Africa’s limited genomic representation constrains AI-enabled AMR detection and prediction. A novel idea of “SAGA-Africa (Streptococcus agalactiae Genomic Alert-Africa)” was proposed, which calls for regional genomic capacity strengthening, mandatory cross-border sequence virtual sharing, alongside installing mobile genomic units in endemic zones.
| eISSN: | 2654-1459 |
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