Building genomic capacity for precision health in Africa

Olono, Alhaji; Mitesser, Vera; Happi, Anise; Happi, Christian

doi:10.1038/s41591-024-03081-9

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…However, as more data emerges, the model will need to be updated using the workflow we have provided. This tool is a significant contribution to bioinformatics capacity on the African continent, a capacity that several African researchers have been calling for in recent times (Nembaware et al, 2023;Olono et al, 2024;Sharaf et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Reveals Key Glycoprotein Mutations and Rapidly Assigns Lassa Virus Lineages

Daodu,

Uwe-Ulrich,

Kühnert

2024

Preprint

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Lassa fever, caused by the Lassa virus (LASV), has led to numerous fatalities in West Africa and cases exported intercontinentally since its discovery in 1969. Currently, there are no approved vaccines, with recent research focusing on immunotherapy. Lassa virus is grouped into different lineages that circulate in specific geographical areas, elicit varying immune responses, and display distinct pathophysiological effects. Therefore, investigating the genetic differences between these lineages is crucial. Here, we analyzed the LASV glycoprotein, the only surface protein, using statistics, machine learning, and phylogenetics to identify key differences between Nigerian lineages and those endemic to other West African countries. We found that amino acid positions near the stable signal peptide cleavage site and sites impacting immune recognition, such as those between positions 59 and 76, were highly variable among the lineages. Additionally, we discovered that Lineage II and Lineage III sequences are one codon shorter than Lineage IV sequences, due to a codon insertion in positions 178-180, corresponding to amino acid position 60. This may explain the structural and phenotypical differences between the lineages. To quickly identify which lineages cause emerging outbreaks or exported infections, we also developed a highly accurate lineage classification tool based on machine learning.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Reveals Key Glycoprotein Mutations and Rapidly Assigns Lassa Virus Lineages

Daodu,

Uwe-Ulrich,

Kühnert

2024

Preprint

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Global solidarity in genomic surveillance improves early detection of respiratory virus threats

de Jong,

Nichols,

de Ruijter

et al. 2023

Preprint

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SummaryPublic health interventions for respiratory virus outbreaks increasingly rely on genomic sequencing for the rapid identification of new (variant) viruses1–5. However, global sequencing efforts are unevenly distributed6–9, with some high-income countries sequencing at >100,000 times the rate of many low-income countries. Given the importance of virus genomic sequencing and substantial global disparities in sequencing capacities, there is a need for meaningful minimum sequencing targets and functional upper bounds that maximise resource efficiency1,2,8,10,11. Here, using mathematical models and analyses of data on global SARS-CoV-2 sequencing output in 2022, we show that increases in sequencing rates typical of low-income countries are >100-fold more effective at reducing time to detection of new variants than increases from rates typical of high-income countries. We find that relative to 2022 sequencing rates, establishing a minimum respiratory virus sequencing capacity of two sequences per million people per week (S/M/wk) with a two-week time from sample collection to sequence deposition in all countries, while simultaneously capping sequencing rates at 30 S/M/wk in all countries, could reduce mean time to first variant detection globally by weeks-to-months while also reducing global sequencing output by >60%. Our results show that investing in a minimum global respiratory virus sequencing capacity is far more effective at improving variant surveillance than expanding local sequencing efforts in countries with existing high-intensity respiratory virus surveillance programs and can guide rightsizing of global respiratory virus genomic surveillance infrastructure.

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Genomics and Bioinformatics in One Health: Transdisciplinary Approaches for Health Promotion and Disease Prevention

Scarpa,

Casu

2024

IJERPH

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The One Health concept underscores the interconnectedness of human, animal, and environmental health, necessitating an integrated, transdisciplinary approach to tackle contemporary health challenges. This perspective paper explores the pivotal role of genomics and bioinformatics in advancing One Health initiatives. By leveraging genomic technologies and bioinformatics tools, researchers can decode complex biological data, enabling comprehensive insights into pathogen evolution, transmission dynamics, and host-pathogen interactions across species and environments (or ecosystems). These insights are crucial for predicting and mitigating zoonotic disease outbreaks, understanding antimicrobial resistance patterns, and developing targeted interventions for health promotion and disease prevention. Furthermore, integrating genomic data with environmental and epidemiological information enhances the precision of public health responses. Here we discuss case studies demonstrating successful applications of genomics and bioinformatics in One Health contexts, such as including data integration, standardization, and ethical considerations in genomic research. By fostering collaboration among geneticists, bioinformaticians, epidemiologists, zoologists, and data scientists, the One Health approach can harness the full potential of genomics and bioinformatics to safeguard global health. This perspective underscores the necessity of continued investment in interdisciplinary education, research infrastructure, and policy frameworks to effectively employ these technologies in the service of a healthier planet.

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Building genomic capacity for precision health in Africa

Cited by 3 publications

References 48 publications

Machine Learning Reveals Key Glycoprotein Mutations and Rapidly Assigns Lassa Virus Lineages

Machine Learning Reveals Key Glycoprotein Mutations and Rapidly Assigns Lassa Virus Lineages

Global solidarity in genomic surveillance improves early detection of respiratory virus threats

Genomics and Bioinformatics in One Health: Transdisciplinary Approaches for Health Promotion and Disease Prevention

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