Human microbiota research in Africa: a systematic review reveals gaps and priorities for future research.

BACKGROUND: The role of the human microbiome in health and disease is an emerging and important area of research; however, there is a concern that African populations are under-represented in human microbiome studies. We, therefore, conducted a systematic survey of African human microbiome studies to provide an overview and identify research gaps. Our secondary objectives were: (i) to determine the number of peer-reviewed publications; (ii) to identify the extent to which the researches focused on diseases identified by the World Health Organization [WHO] State of Health in the African Region Report as being the leading causes of morbidity and mortality in 2018; (iii) to describe the extent and pattern of collaborations between researchers in Africa and the rest of the world; and (iv) to identify leadership and funders of the studies. METHODOLOGY: We systematically searched Medline via PubMed, Scopus, CINAHL, Academic Search Premier, Africa-Wide Information through EBSCOhost, and Web of Science from inception through to 1st April 2020. We included studies that characterized samples from African populations using next-generation sequencing approaches. Two reviewers independently conducted the literature search, title and abstract, and full-text screening, as well as data extraction. RESULTS: We included 168 studies out of 5515 records retrieved. Most studies were published in PLoS One (13%; 22/168), and samples were collected from 33 of the 54 African countries. The country where most studies were conducted was South Africa (27/168), followed by Kenya (23/168) and Uganda (18/168). 26.8% (45/168) focused on diseases of significant public health concern in Africa. Collaboration between scientists from the United States of America and Africa was most common (96/168). The first and/or last authors of 79.8% of studies were not affiliated with institutions in Africa. Major funders were the United States of America National Institutes of Health (45.2%; 76/168), Bill and Melinda Gates Foundation (17.8%; 30/168), and the European Union (11.9%; 20/168). CONCLUSIONS: There are significant gaps in microbiome research in Africa, especially those focusing on diseases of public health importance. There is a need for local leadership, capacity building, intra-continental collaboration, and national government investment in microbiome research within Africa. Video Abstract.

ANIMA: Association network integration for multiscale analysis.

Contextual functional interpretation of -omics data derived from clinical samples is a classical and difficult problem in computational systems biology. The measurement of thousands of data points on single samples has become routine but relating 'big data' datasets to the complexities of human pathobiology is an area of ongoing research. Complicating this is the fact that many publicly available datasets use bulk transcriptomics data from complex tissues like blood. The most prevalent analytic approaches derive molecular 'signatures' of disease states or apply modular analysis frameworks to the data. Here we describe ANIMA (association network integration for multiscale analysis), a network-based data integration method using clinical phenotype and microarray data as inputs. ANIMA is implemented in R and Neo4j and runs in Docker containers. In short, the build algorithm iterates over one or more transcriptomics datasets to generate a large, multipartite association network by executing multiple independent analytic steps (differential expression, deconvolution, modular analysis based on co-expression, pathway analysis) and integrating the results. Once the network is built, it can be queried directly using Cypher (a graph query language), or by custom functions that communicate with the graph database via language-specific APIs. We developed a web application using Shiny, which provides fully interactive, multiscale views of the data. Using our approach, we show that we can reconstruct multiple features of disease states at various scales of organization, from transcript abundance patterns of individual genes through co-expression patterns of groups of genes to patterns of cellular behaviour in whole blood samples, both in single experiments as well in meta-analyses of multiple datasets.