Fourie Joubert

Neonatal encephalopathy suspected to be due to hypoxic ischaemic encephalopathy (NESHIE) carries the risk of death or severe disability (cognitive defects and cerebral palsy). Previous genetic studies on NESHIE have predominantly focused on exomes or targeted genes. The objective of this study was to identify genetic variants associated with moderate–severe NESHIE through whole-genome, unbiased analysis. Variant filtering and prioritization were performed, followed by association testing both on a case–control basis and to compare the grades of severity and/or progression. Association testing on neonates with NESHIE (N = 172) and ancestry-matched controls (N = 288) produced 71 significant genetic variants (false discovery rate corrected p-value < 6.2 × 10⁻⁴), all located in non-coding regions and not previously implicated in NESHIE. Disease-associated variants in non-coding regions are considered to affect regulatory functions, possibly by modifying gene expression, promoters, enhancers, or DNA structure. The most significant variant was at position 6:162010973 in the Parkin RBR E3 ubiquitin protein ligase (PRKN) intron. Intronic variants were also identified in genes involved in inflammatory processes (SLCO3A1), DNA repair (ZGRF1), synaptogenesis (CNTN5), haematopoiesis (ASXL2), and the transcriptional response to hypoxia (PADI4). Ten variants were associated with a higher severity or lack of improvement in NESHIE, including one in ADAMTS3, which encodes a procollagen amino protease with a role in angiogenesis and lymphangiogenesis. This analysis represents one of the first efforts to analyze whole-genome data to investigate the genetic complexity of NESHIE in diverse ethnolinguistic groups of African origin and provides direction for further study.

Objectives: This study focuses on identifying variations in selected CYP genes related to treatment responses in patients with HIV in African populations by investigating variant characteristics and effects in African cohorts. Design: Cytochrome P450 (CYP) 2A6, 2B6, and Uridine 5’-diphospho-glucuronosyltransferase (UGT) 2B7 allele frequencies were studied using public-domain datasets obtained from the 1000 Genomes Phase 3 project, the African Genome Variation Project (AGVP), and the South African Human Genome Programme (SAHGP). Methods: Variant annotations were performed using self-identified ethnicities to conduct allele frequency analysis in a population-stratification-sensitive manner. The NCBI DB-SNP database was used to identify documented variants and standard frequencies, and the E! Ensembl Variant Effect Predictor tool was used to perform the prediction of possible deleterious variants. Results: A total of 4468 variants were identified across 3676 individuals following pre-filtering. Seventy-one variants were identified at an allelic frequency (1% or more in at least one population), which were predicted to be linked to existing disease associations and, in some cases, linked to drug metabolisms. This list was further studied to identify 23 alleles with disease considerations found at significantly different frequencies in one or more populations. Conclusions: This study describes allele frequencies observed in African populations at significantly different frequencies relative to at least one other reference population and identifies a subset of variants of clinical interest. Despite the inclusion of mixed sequence coverage datasets, the variants identified pose notable avenues for future inquiries. A subset of variants of clinical interest with statistically significant inter-population frequency differences was identified for further inspection, which provides evidence of an African population-specific variant frequency profile. This study highlights the need for additional research and African genetics data given the presence of this unique frequency profile to better facilitate the genetic pre-screening of patients as a standard of practice in HIV care, particularly on the African continent where HIV is highly prevalent.

Maize yields worldwide are limited by foliar diseases that could be fungal, oomycete, bacterial, or viral in origin. Correct disease identification is critical for farmers to apply the correct control measures, such as fungicide sprays. Deep learning has the potential for automated disease classification from images of leaf symptoms. We aimed to develop a classifier to identify gray leaf spot (GLS) disease of maize in field images where mixed diseases were present (18,656 images after augmentation). In this study, we compare deep learning models trained on mixed disease field images with and without background subtraction. Performance was compared with models trained on PlantVillage images with single diseases and uniform backgrounds. First, we developed a modified VGG16 network referred to as “GLS_net” to perform binary classification of GLS, which achieved a 73.4% accuracy. Second, we used MaskRCNN to dynamically segment leaves from backgrounds in combination with GLS_net to identify GLS, resulting in a 72.6% accuracy. Models trained on PlantVillage images were 94.1% accurate at GLS classification with the PlantVillage testing set but performed poorly with the field image dataset (55.1% accuracy). In contrast, the GLS_net model was 78% accurate on the PlantVillage testing set. We conclude that deep learning models trained with realistic mixed disease field data obtain superior degrees of generalizability and external validity when compared to models trained using idealized datasets.

Phytophthora cinnamomi is an economically important plant pathogen that has caused devastating losses to the avocado industry worldwide. To facilitate penetration and successful colonization of the host plant, pathogens have been reported to secrete polygalacturonases (PGs). Although a large PG gene family has been reported in P. cinnamomi, in-depth bioinformatics analyses and characterization of these genes is still lacking. In this study we used bioinformatics tools and molecular biology techniques to identify and characterize endopolygalacturonases in the genome of a South African P. cinnamomi isolate, GKB4. We identified 37 PGs, with 19 characteristics of full-length PGs. Although eight PcPGs were induced in planta during infection, only three showed significant up- and down-regulation when compared with in vitro mycelial growth, suggesting their possible roles in infection. The phylogenetic analysis of PcPGs showed both gain and loss of introns in the evolution of PGs in P. cinnamomi. Furthermore, 17 PGs were related to characterized PGs from oomycete species, providing insight on possible function. This study provides new data on endoPGs in P. cinnamomi and the evolution of introns in PcPG genes. We also provide a baseline for future functional characterization of PGs suspected to contribute to P. cinnamomi pathogenicity/virulence in avocado.

Since the discovery of the breast cancer susceptibility genes, BRCA1 and BRCA2, various other genes conferring an increased risk for breast cancer have been identified. Studies to evaluate sequence variants in cancer predisposition genes among women of African ancestry are limited and mostly focused on BRCA1 and BRCA2. To characterize germline sequence variants in cancer susceptibility genes, we analysed a cohort of 165 South African women of self-identified African ancestry diagnosed with breast cancer, who were unselected for family history of cancer. With the exception of four cases, all others were previously investigated for BRCA1 and BRCA2 deleterious variants, and were negative for pathogenic variants. We utilized the Illumina TruSight cancer panel for targeted sequencing of 94 cancer susceptibility genes. A total of 3.6% of patients carried a pathogenic/likely pathogenic variant in a known breast cancer susceptibility gene: 1.2% in BRCA1, 0.6% in each of BRCA2, ATM, CHEK2 and PALB, none of whom had any family history of breast cancer. The mean age of patients who carried deleterious variant in BRCA1/BRCA2 was 39 years and 8 months compared to 47 years and 3 months among women who carried a deleterious variant in other breast cancer susceptibility genes.

The potential for human adipose-derived stromal cells (hASCs) to be used as a therapeutic product is being assessed in multiple clinical trials. However, much is still to be learned about these cells before they can be used with confidence in the clinical setting. An inherent characteristic of hASCs that is not well understood is their heterogeneity. The aim of this exploratory study was to characterize the heterogeneity of freshly isolated hASCs after two population doublings (P2) using single-cell transcriptome analysis. A minimum of two subpopulations were identified at P2. A major subpopulation was identified as contractile cells which, based on gene expression patterns, are likely to be pericytes and/or vascular smooth muscle cells (vSMCs).

The Pan-African bioinformatics network, H3ABioNet, comprises 27 research institutions in 17 African countries. H3ABioNet is part of the Human Health and Heredity in Africa program (H3Africa), an African-led research consortium funded by the US National Institutes of Health and the UK Wellcome Trust, aimed at using genomics to study and improve the health of Africans. A key role of H3ABioNet is to support H3Africa projects by building bioinformatics infrastructure such as portable and reproducible bioinformatics workflows for use on heterogeneous African computing environments. Processing and analysis of genomic data is an example of a big data application requiring complex interdependent data analysis workflows. Such bioinformatics workflows take the primary and secondary input data through several computationally-intensive processing steps using different software packages, where some of the outputs form inputs for other steps. Implementing scalable, reproducible, portable and easy-to-use workflows is particularly challenging. H3ABioNet has built four workflows to support (1) the calling of variants from high-throughput sequencing data; (2) the analysis of microbial populations from 16S rDNA sequence data; (3) genotyping and genome-wide association studies; and (4) single nucleotide polymorphism imputation. A week-long hackathon was organized in August 2016 with participants from six African bioinformatics groups, and US and European collaborators. Two of the workflows are built using the Common Workflow Language framework (CWL) and two using Nextflow. All the workflows are containerized for improved portability and reproducibility using Docker, and are publicly available for use by members of the H3Africa consortium and the international research community. The H3ABioNet workflows have been implemented in view of offering ease of use for the end user and high levels of reproducibility and portability, all while following modern state of the art bioinformatics data processing protocols. The H3ABioNet workflows will service the H3Africa consortium projects and are currently in use. All four workflows are also publicly available for research scientists worldwide to use and adapt for their respective needs. The H3ABioNet workflows will help develop bioinformatics capacity and assist genomics research within Africa and serve to increase the scientific output of H3Africa and its Pan-African Bioinformatics Network.

The genomes of Armillaria fuscipes, Ceratocystiopsis minuta, Ceratocystis adiposa, Endoconidiophora laricicola, E. polonica, and Penicillium freii DAOMC 242723 are presented in this genome announcement. These six genomes are from plant pathogens and otherwise economically important fungal species. The genome sizes range from 21 Mb in the case of Ceratocystiopsis minuta to 58 Mb for the basidiomycete Armillaria fuscipes. These genomes include the first reports of genomes for the genus Endoconidiophora. The availability of these genome data will provide opportunities to resolve longstanding questions regarding the taxonomy of species in these genera. In addition these genome sequences through comparative studies with closely related organisms will increase our understanding of how these pathogens cause disease.

Bioinformatics is now a critical skill in many research and commercial environments as biological data are increasing in both size and complexity. South African researchers recognized this need in the mid-1990s and responded by working with the government as well as international bodies to develop initiatives to build bioinformatics capacity in the country. Significant injections of support from these bodies provided a springboard for the establishment of computational biology units at multiple universities throughout the country, which took on teaching, basic research and support roles. Several challenges were encountered, for example with unreliability of funding, lack of skills, and lack of infrastructure. However, the bioinformatics community worked together to overcome these, and South Africa is now arguably the leading country in bioinformatics on the African continent. Here we discuss how the discipline developed in the country, highlighting the challenges, successes, and lessons learnt.

Carbohydrate metabolism is a key feature of vascular plant architecture, and is of particular importance in large woody species, where lignocellulosic biomass is responsible for bearing the bulk of the stem and crown. Since Carbohydrate Active enZymes (CAZymes) in plants are responsible for the synthesis, modification and degradation of carbohydrate biopolymers, the differences in gene copy number and regulation between woody and herbaceous species have been highlighted previously. There are still many unanswered questions about the role of CAZymes in land plant evolution and the formation of wood, a strong carbohydrate sink. Here, twenty-two publically available plant genomes were used to characterize the frequency, diversity and complexity of CAZymes in plants. We find that a conserved suite of CAZymes is a feature of land plant evolution, with similar diversity and complexity regardless of growth habit and form. In addition, we compared the diversity and levels of CAZyme gene expression during wood formation in trees using mRNA-seq data from two distantly related angiosperm tree species Eucalyptus grandis and Populus trichocarpa, highlighting the major CAZyme classes involved in xylogenesis and lignocellulosic biomass production. CAZyme domain ratio across embryophytes is maintained, and the diversity of CAZyme domains is similar in all land plants, regardless of woody habit. The stoichiometric conservation of gene expression in woody and non-woody tissues of Eucalyptus and Populus are indicative of gene balance preservation.