Biology subjects

Opiyo, S. O.

Publications and source records attributed to Opiyo, S. O..

3 recordsLinked to original sources

bioRxiv2026

Using GIS Dashboards to highlight AMR data disparities in Africa for Policy, Research, and Public Health

Antimicrobial resistance (AMR) continues to pose a major public health threat across Africa, yet available surveillance data remain highly fragmented across private, public, and academic sources. This study analysed continent-wide AMR surveillance patterns by integrating datasets from multiple independent repositories and visualising them through interactive Geographic Information System (GIS) dashboards. The objective was to generate an integrated evidence base that highlights resistance patterns, surveillance disparities, reporting gaps, and opportunities for improved AMR monitoring across Africa. Data were compiled from major private AMR surveillance programmes including Pfizers ATLAS, GSKs SOAR, Johnson & Johnsons DREAM, Venatorxs GEARS, and Shionogis SIDERO-WT covering the period 2004-2022. Public datasets from the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) and the Fleming Funds Mapping Antimicrobial Resistance and Antimicrobial Use Partnership (MAAP) were incorporated for 2016-2020, together with published AMR studies conducted between 2010 and 2024. Datasets were harmonised to align key variables including bacterial species, isolate identifiers, antibiotics tested, surveillance source, geographical location, and categorical AMR outcomes while preserving the original structure of the contributing datasets. Interactive dashboards were developed using R Shiny to support spatial visualisation and dynamic analytical exploration of resistance patterns, temporal trends, species distribution, and country-level surveillance coverage. Descriptive analyses including means, standard deviations, medians, interquartile ranges (IQR), frequency distributions, Gini coefficients, Shannon entropy, Herfindahl-Hirschman Index (HHI), and Lorenz curves were used to assess inequality and concentration in country-level AMR reporting across surveillance systems. The integrated analyses revealed substantial heterogeneity and concentration in AMR surveillance reporting across Africa, reflecting major differences in surveillance intensity, laboratory infrastructure, reporting systems, and diagnostic capacity across countries. Private datasets demonstrated broader antibiotic panels and longer temporal coverage, whereas public datasets exhibited substantial gaps in country participation and pathogen-antibiotic representation. Published AMR studies additionally highlighted important surveillance information absent from formal surveillance databases. By integrating multiple streams of AMR evidence, this study demonstrates the value of interactive GIS dashboards as exploratory and updateable surveillance-support tools for improving visibility of fragmented AMR datasets, identifying surveillance disparities, supporting geographically informed interpretation of resistance trends, and strengthening future AMR surveillance harmonisation efforts across Africa.

bioinformatics↗

bioRxiv2020

Metagenomic survey unravel diversity of biogas microbiome with potential to enhance its' productivity in Kenya

The obstacle to optimal utilization of biogas technology is poor understanding of biogas microbiome diversities over a wide geographical coverage. We performed random shotgun sequencing on twelve environmental samples. A randomized complete block design was utilized to assign the twelve biogas reactor treatments to four blocks, within eastern and central regions of Kenya. We obtained 42 million paired-end reads that were annotated against sixteen reference databases using two ENVO ontologies, prior to {beta}-diversities studies. We identified 37 phyla, 65 classes and 132 orders of micro-organisms. Bacteria dominated the microbiome and comprised of 28 phyla, 42 classes and 92 orders, conveying substrates versatility in the treatments. Though, fungi and Archaea comprised of only 5 phyla, the fungi were richer; suggesting the importance of hydrolysis and fermentation in biogas production systems. High {beta}-diversity within the taxa was largely linked to communities metabolic capabilities. Clostridiales and Bacteroidales, the most prevalent guilds, metabolize organic macromolecules. The identified affiliates of Cytophagales, Alteromonadales, Flavobacteriales, Fusobacteriales, Deferribacterales, Elusimicrobiales, Chlamydiales, Synergistales to mention but few, also catabolize macromolecules into smaller substrates to conserve energy. Furthermore,{delta} -Proteobacteria, Gloeobacteria and Clostridia affiliates syntrophically regulate PH2 and reduce metal to provide reducing equivalents. Methanomicrobiales and other Methanomicrobia species were the most prevalence Archaea, converting formate, CO2(g), acetate and methylated substrates into CH4(g). Thermococci, Thermoplasmata and Thermoprotei were among the sulfur and other metal reducing Archaea that contributed to redox balancing and other metabolism within treatments. Eukaryotes, mainly fungi were the least abundant guild, comprised largely Ascomycota and Basidiomycota species. Chytridiomycetes, Blastocladiomycetes and Mortierellomycetes were among the rare species, suggesting their metabolic and substrates limitations. Generally, we observed that environmental and treatment perturbations influenced communities abundance, {beta}-diversity and reactor performance largely through stochastic effect. The study of the diversity of the biogas microbiomes over wide environmental variables and the productivity of biogas reactor systems has provided insights into better management strategies that may ameliorate biochemical limitations to effective biogas production. Author SummaryThe failure of biochemical reactions in biogas producing systems is a common problem and results from poor functioning of the inhabiting micro-organisms. A poor understanding of the global diversities of these micro-organisms and lack of information on the link between environmental variables, biogas production, and community composition, contrains the development of strategies that can ameliorate these biochemical issues. We have integrated sequencing-by-synthesis technology and intensive computational approaches to reveal metacommunities in the studied reactor treatments. The identified communities were compared with the treatments phenotypic and environmental data in an attempt to fill the existing knowledge gaps on biogas microbiomes and their production capacities. We present 132 biogas taxonomic profiles systematically and comparatively, linking the abundance with the identified environmental variables. The local composition of microbiome and variations in abundance were also linked to the observed differences in biogas productivity, suggesting the possible cause of the observed variations. The detailed information presented in this study can aid in the genetic manipulation or formulation of optimal microbial ratios to improve their effectiveness in biogas production.

biochemistry↗

bioRxiv2018

A Genome-Wide Association Study Identifies SNP Markers for Virulence in Magnaporthe oryzae Isolates from Sub-Saharan Africa

The fungal phytopathogen Magnaporthe oryzae causes blast disease in cereals such as rice and finger millet worldwide. In this study, we assessed genetic diversity of 160 isolates from nine sub-Saharan Africa (SSA) and other principal rice producing countries and conducted a genome-wide association study (GWAS) to identify the genomic regions associated with virulence of M. oryzae. GBS of isolates provided a large and high-quality 617K single nucleotide polymorphism (SNP) dataset. Disease ratings for each isolate was obtained by inoculating them onto differential lines and locally-adapted rice cultivars. Genome-wide association studies were conducted using the GBS dataset and sixteen disease rating datasets. Principal Component Analysis (PCA) was used an alternative to population structure analysis for studying population stratification from genotypic data. A significant association between disease phenotype and 528 SNPs was observed in six GWA analyses. Homology of sequences encompassing the significant SNPs was determined to predict gene identities and functions. Seventeen genes recurred in six GWA analyses, suggesting a strong association with virulence. Here, the putative genes/genomic regions associated with the significant SNPs are presented.

genetics↗