Alan Christoffels

Summary: Africa’s diverse pathogen and human genetics present significant opportunities for genomic medicine research using artificial intelligence (AI). We surveyed 250 genomics researchers across 46 of the 55 African Union Member States to assess the state of AI use. The survey revealed that only 4% of respondents reported high familiarity with the application of AI for genomics and bioinformatics.

Microbial data sharing underlies evidence-based microbial research, as well as pathogen surveillance and analysis essential to public health. While the need for data sharing was highlighted during the SARS-CoV-2 pandemic, some concerns regarding secondary data use have also surfaced. Although general guidelines are available for data sharing, we note the absence of a set of established, universal, unambiguous and accessible principles to guide the secondary use of microbial data. Here, we propose the Public Health Alliance for Genomic Epidemiology (PHA4GE) Microbial Data-Sharing Accord to consolidate consensus norms and accepted practices for the secondary use of microbial data. The Accord provides a set of seven simple, baseline principles to address key concerns that may arise for researchers providing microbial datasets for secondary use and to guide responsible use by data users. By providing clear rules for secondary use of microbial data, the Accord can increase confidence in sharing by data providers and protect against data mis-use during secondary analyses.

High-income countries have a wealth of genomics expertise that can be rapidly activated to deal with disease threats. African countries should invest in a federated data-management system for genomics epidemiology to deal with such threats better.

Rift Valley fever (RVF) is a febrile vector-borne disease endemic in Africa and continues to spread in new territories. It is a climate-sensitive disease mostly triggered by abnormal rainfall patterns. The disease is associated with high mortality and morbidity in both humans and livestock. RVF is caused by the Rift Valley fever virus (RVFV) of the genus Phlebovirus in the family Phenuiviridae. It is a tripartite RNA virus with three genomic segments: small (S), medium (M) and large (L). Pathogen genomic sequencing is becoming a routine procedure and a powerful tool for understanding the evolutionary dynamics of infectious organisms, including viruses. Inspired by the utility of amplicon-based sequencing demonstrated in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and Ebola, Zika and West Nile viruses, we report an RVFV sample preparation based on amplicon multiplex polymerase chain reaction (amPCR) for template enrichment and reduction of background host contamination. The technology can be implemented rapidly to characterize and genotype RVFV during outbreaks in a near-real-time manner. To achieve this, we designed 74 multiplex primer sets covering the entire RVFV genome to specifically amplify the nucleic acid of RVFV in clinical samples from an animal tissue. Using this approach, we demonstrate achieving complete RVFV genome coverage even from samples containing a relatively low viral load. We report the first primer scheme approach of generating multiplex primer sets for a tripartite virus which can be replicated for other segmented viruses.

Investment in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing in Africa over the past year has led to a major increase in the number of sequences that have been generated and used to track the pandemic on the continent, a number that now exceeds 100,000 genomes. Our results show an increase in the number of African countries that are able to sequence domestically and highlight that local sequencing enables faster turnaround times and more-regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and illuminate the distinct dispersal dynamics of variants of concern—particularly Alpha, Beta, Delta, and Omicron—on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve while the continent faces many emerging and reemerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century.

An operon is a set of adjacent genes which are transcribed into a single messenger RNA. Operons allow prokaryotes to efficiently circumvent environmental stresses. It is estimated that about 60% of the Mycobacterium tuberculosis genome is arranged into operons, which makes them interesting drug targets in the face of emerging drug resistance. We therefore developed COSMO - a tool for operon prediction in M. tuberculosis using RNA-seq data. We analyzed four algorithmic parameters and benchmarked COSMO against two top performing operon predictors. COSMO outperformed both predictors in its accuracy and in its ability to distinguish operons activated under distinct conditions. Author Summary: Operons may be important drug targets for the development of effective anti-microbials to combat the emerging, global drug resistance challenge. However, there is a shortage of known Mycobacterium tuberculosis (Mtb) operons. This is exacerbated by the fact that current operon predictors are not optimized for the unique genome of Mtb. COSMO removes the limitations imposed by using the constraints of a specific organism’s genome and exploits RNA-seq data instead. This allows COSMO to more accurately predict full-length operons in Mtb, and it also avails COSMO to other microorganisms for the same purpose.

The growing demands on protein producers and the dwindling available resources have made Hermetia illucens (the black soldier fly, BSF) an economically important species. Insights into the genome of this insect will better allow for robust breeding protocols, and more efficient production to be used as a replacement of animal feed protein. The use of microRNA as a method to understand how gene regulation allows insect species to adapt to changes in their environment, has been established in multiple species. The baseline and life stage expression levels established in this study, allow for insight into the development and sex-linked microRNA regulation in BSF. To accomplish this, microRNA was extracted and sequenced from 15 different libraries with each life stage in triplicate. Of the total 192 microRNAs found, 168 were orthologous to known arthropod microRNAs and 24 microRNAs were unique to BSF. Twenty-six of the 168 microRNAs conserved across arthropods had a statistically significant (p < 0.05) differential expression between Egg to Larval stages. The development from larva to pupa was characterized by 16 statistically significant differentially expressed microRNA. Seven and 9 microRNA were detected as statistically significant between pupa to adult female and pupa to adult male, respectively. All life stages had a nearly equal split between up and down regulated microRNAs. Ten of the unique 24 miRNA were detected exclusively in one life stage. The egg life stage expressed five microRNA (hil-miR-m, hil-miR-p, hil-miR-r, hil-miR-s, and hil-miR-u) not seen in any other life stages. The female adult and pupa life stages expressed one miRNA each hil-miR-h and hil-miR-ac respectively. Both male and female adult life stages expressed hil-miR-a, hil-miR-b, and hil-miR-y. There were no unique microRNAs found only in the larva stage. Twenty-two microRNAs with 56 experimentally validated target genes in the closely related Drosophila melanogaster were identified. Thus, the microRNA found display the unique evolution of BSF, along with the life stages and potential genes to target for robust mass rearing. Understanding of the microRNA expression in BSF will further their use in the crucial search for alternative and sustainable protein sources.