FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology.

In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist's toolbox to enhance the efficiency of both hit optimization and candidate design. Both computational methods come with their own set of limitations, and they are often used independently of each other. ML's capability to screen extensive compound libraries expediently is tempered by its reliance on quality data, which can be scarce especially during early-stage optimization. Contrarily, physics-based approaches like free energy perturbation (FEP) are frequently constrained by low throughput and high cost by comparison; however, physics-based methods are capable of making highly accurate binding affinity predictions. In this study, we harnessed the strength of FEP to overcome data paucity in ML by generating virtual activity data sets which then inform the training of algorithms. Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. Throughout the paper, we emphasize key mechanistic considerations that must be taken into account when aiming to augment data sets and lay the groundwork for successful implementation. Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. We believe that the physics-based augmentation of ML will significantly benefit drug discovery, as these techniques continue to evolve.

Pharmacogenetics of CYP2A6, CYP2B6, and UGT2B7 in the Context of HIV Treatments in African Populations.

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.

The NESHIE and CP Genetics Resource (NCGR): A database of genes and variants reported in neonatal encephalopathy with suspected hypoxic ischemic encephalopathy (NESHIE) and consequential cerebral palsy (CP).

Neonatal encephalopathy (NE) with suspected hypoxic ischaemic encephalopathy (HIE) (NESHIE) is a complex syndrome occurring in newborns, characterised by altered neurological function. It has been suggested that genetic variants may influence NESHIE susceptibility and outcomes. Unlike NESHIE, for which a limited number of genetic studies have been performed, many studies have identified genetic variants associated with cerebral palsy (CP), which can develop from severe NESHIE. Identifying variants in patients with CP, as a consequence of NESHIE, may provide a starting point for the identification of genetic variants associated with NESHIE outcomes. We have constructed NCGR (NESHIE and CP Genetics Resource), a database of genes and variants reported in patients with NESHIE and CP (where relevant to NESHIE), for the purpose of collating and comparing genetic findings between the two conditions. In this paper we describe the construction and functionality of NCGR. Furthermore, we demonstrate how NCGR can be used to prioritise genes and variants of potential clinical relevance that may underlie a genetic predisposition to NESHIE and contribute to an understanding of its pathogenesis.

Deep Learning Diagnostics of Gray Leaf Spot in Maize under Mixed Disease Field Conditions.

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.

The Identification and Characterization of Endopolygalacturonases in a South African Isolate of Phytophthora cinnamomi.

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.

Coronavirus Host Genetics South Africa (COHG-SA) database-a variant database for gene regions associated with SARS-CoV-2 outcomes.

The SARS-CoV-2 virus is responsible for the COVID-19 global public health emergency, and the disease it causes is highly variable in its clinical presentation. Clinical phenotypes are heterogeneous both in terms of presentation of symptoms in the host and response to therapy. Several studies and initiatives have been established to analyse and review host genetic epidemiology associated with COVID-19. Our research group curated these articles into a web-based database using the python application-server framework Django. The database provides a searchable research tool describing current literature surrounding COVID-19 host genetic factors associated with disease outcome. This paper describes the COHG-SA database and provides an overview of the analyses that can be derived from these data.

Germline sequence variants contributing to cancer susceptibility in South African breast cancer patients of African ancestry.

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.

Single-Cell Transcriptome Analysis of Human Adipose-Derived Stromal Cells Identifies a Contractile Cell Subpopulation.

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).

Limited diversity in the CD8+ antigen-coding loci in Theileria parva parasites from cattle from southern and eastern Africa.

Theileria parva infections in cattle causes huge economic losses in the affected African countries, directly impacting the livelihood of the poor small-holder farmers. The current immunization protocol using live sporozoites in eastern Africa, is among the control measures designed to limit T. parva infections in cattle. However, the ability of the immune protection induced by this immunization to protect against field parasites has been compromised by the diversity of the parasite involving the schizont antigen genes. Previous studies have reported on the antigenic diversity of T. parva parasites from southern and eastern Africa, however, similar reports on T. parva parasites particularly from cattle from southern Africa remains scanty, due to the self-limiting nature of Corridor disease. Thus, we evaluated the diversity of CD8+ T-cell regions of ten schizont antigen genes in T. parva parasites associated with Corridor disease and East Coast fever (ECF) from southern and eastern Africa respectively. Regions of schizont antigen (TpAg) genes containing the CD8+ T-cell epitopes (CTL determinants) were amplified from genomic DNA extracted from blood of T. parva positive samples, cloned and sequenced. The results revealed limited diversity between the two parasite groups from cattle from southern and eastern Africa, defying the widely accepted notion that antigen-encoding loci in cattle-derived parasites are conserved, while in buffalo-derived parasites, they are extensively variable. This suggests that only a sub-population of parasites is successfully transmitted from buffalo to cattle, resulting in the limited antigenic diversity in Corridor disease parasites. Tp4, Tp5, Tp7 and Tp8 showed limited to absence of diversity in both parasite groups, suggesting the need to further investigate their immunogenic properties for consideration as candidates for a subunit vaccine. Distinct and common variants of Tp2 were detected among the ECF parasites from eastern Africa indicating evidence of parasite mixing following immunization. This study provides additional information on the comparative diversity of TpAg genes in buffalo- and cattle-derived T. parva parasites from cattle from southern and eastern Africa.

The immuno-oncological implications of insulin.

Emerging evidence has implicated insulin in regulating the phenotypes of various immune cells through canonical downstream signalling effectors of insulin, namely, the PI3K/Akt/mTOR pathway. Notably, these signalling components also exhibit crosstalk with other immune signalling pathways, such as the JAK/STAT pathway (activated by cytokines and growth factors), and, importantly, are also negatively regulated by the immune checkpoint blockers (ICBs), PD-1 and CTLA-4. Here, we point out recent findings, suggesting that insulin may promote a pro-inflammatory phenotype with potential implications on ICB therapy. As an example, the contemporary paradigm holds that, while T cell receptor recognition of distinct MHC-expressed epitopes ensures specificity, co-activation of CD28 along with signal inputs form various cytokines and insulin operates to 'fine-tune' the immune response via PI3K and other downstream signalling molecules. These considerations highlight the urgent need for focused investigations into the role of insulin in regulating immune cell function in the context of ICB therapies.

Functional annotation of putative QTL associated with black tea quality and drought tolerance traits.

The understanding of black tea quality and percent relative water content (%RWC) traits in tea (Camellia sinensis) by a quantitative trait loci (QTL) approach can be useful in elucidation and identification of candidate genes underlying the QTL which has remained to be difficult. The objective of the study was to identify putative QTL controlling black tea quality and percent relative water traits in two tea populations and their F1 progeny. A total of 1,421 DArTseq markers derived from the linkage map identified 53 DArTseq markers to be linked to black tea quality and %RWC. All 53 DArTseq markers with unique best hits were identified in the tea genome. A total of 5,592 unigenes were assigned gene ontology (GO) terms, 56% comprised biological processes, cellular component (29%) and molecular functions (15%), respectively. A total of 84 unigenes in 15 LGs were assigned to 25 different Kyoto Encyclopedia of Genes and Genomes (KEGG) database pathways based on categories of secondary metabolite biosynthesis. The three major enzymes identified were transferases (38.9%), hydrolases (29%) and oxidoreductases (18.3%). The putative candidate proteins identified were involved in flavonoid biosynthesis, alkaloid biosynthesis, ATPase family proteins related to abiotic/biotic stress response. The functional annotation of putative QTL identified in this current study will shed more light on the proteins associated with caffeine and catechins biosynthesis and % RWC. This study may help breeders in selection of parents with desirable DArTseq markers for development of new tea cultivars with desirable traits.

The Draft Genome of Deladenus siricidicola.

The nematode Deladenus siricidicola is used as biological control agent against the invasive woodwasp Sirex noctilio, a serious invasive pest of Pinus plantations globally. The draft genome of this ecologically and economically important entomoparasitic nematode was determined.

Developing reproducible bioinformatics analysis workflows for heterogeneous computing environments to support African genomics.

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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.

Organizing and running bioinformatics hackathons within Africa: The H3ABioNet cloud computing experience.

The need for portable and reproducible genomics analysis pipelines is growing globally as well as in Africa, especially with the growth of collaborative projects like the Human Health and Heredity in Africa Consortium (H3Africa). The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for portable, reproducible pipelines adapted to heterogeneous compute environments, and for the nurturing of technical expertise in workflow languages and containerization technologies. To address this need, in 2016 H3ABioNet arranged its first Cloud Computing and Reproducible Workflows Hackathon, with the purpose of building key genomics analysis pipelines able to run on heterogeneous computing environments and meeting the needs of H3Africa research projects. This paper describes the preparations for this hackathon and reflects upon the lessons learned about its impact on building the technical and scientific expertise of African researchers. The workflows developed were made publicly available in GitHub repositories and deposited as container images on quay.io.

Whole-genome sequencing for an enhanced understanding of genetic variation among South Africans.

The Southern African Human Genome Programme is a national initiative that aspires to unlock the unique genetic character of southern African populations for a better understanding of human genetic diversity. In this pilot study the Southern African Human Genome Programme characterizes the genomes of 24 individuals (8 Coloured and 16 black southeastern Bantu-speakers) using deep whole-genome sequencing. A total of ~16 million unique variants are identified. Despite the shallow time depth since divergence between the two main southeastern Bantu-speaking groups (Nguni and Sotho-Tswana), principal component analysis and structure analysis reveal significant (p < 10-6) differentiation, and FST analysis identifies regions with high divergence. The Coloured individuals show evidence of varying proportions of admixture with Khoesan, Bantu-speakers, Europeans, and populations from the Indian sub-continent. Whole-genome sequencing data reveal extensive genomic diversity, increasing our understanding of the complex and region-specific history of African populations and highlighting its potential impact on biomedical research and genetic susceptibility to disease.

IMA Genome-F 8: Draft genome of Cercospora zeina, Fusarium pininemorale, Hawksworthiomyces lignivorus, Huntiella decipiens and Ophiostoma ips.

The genomes of Cercospora zeina, Fusarium pininemorale, Hawksworthiomyces lignivorus, Huntiella decipiens, and Ophiostoma ips are presented in this genome announcement. Three of these genomes are from plant pathogens and otherwise economically important fungal species. Fusarium pininemorale and H. decipiens are not known to cause significant disease but are closely related to species of economic importance. The genome sizes range from 25.99 Mb in the case of O. ips to 4.82 Mb for H. lignivorus. These genomes include the first reports of a genome from the genus Hawksworthiomyces. The availability of these genome data will allow the resolution of longstanding questions regarding the taxonomy of these species. In addition these genome sequences through comparative studies with closely related organisms will increase our understanding of how these species or close relatives cause disease.

Systems genetics reveals a transcriptional network associated with susceptibility in the maize-grey leaf spot pathosystem.

We used a systems genetics approach to elucidate the molecular mechanisms of the responses of maize to grey leaf spot (GLS) disease caused by Cercospora zeina, a threat to maize production globally. Expression analysis of earleaf samples in a subtropical maize recombinant inbred line population (CML444 × SC Malawi) subjected in the field to C. zeina infection allowed detection of 20 206 expression quantitative trait loci (eQTLs). Four trans-eQTL hotspots coincided with GLS disease QTLs mapped in the same field experiment. Co-expression network analysis identified three expression modules correlated with GLS disease scores. The module (GY-s) most highly correlated with susceptibility (r = 0.71; 179 genes) was enriched for the glyoxylate pathway, lipid metabolism, diterpenoid biosynthesis and responses to pathogen molecules such as chitin. The GY-s module was enriched for genes with trans-eQTLs in hotspots on chromosomes 9 and 10, which also coincided with phenotypic QTLs for susceptibility to GLS. This transcriptional network has significant overlap with the GLS susceptibility response of maize line B73, and may reflect pathogen manipulation for nutrient acquisition and/or unsuccessful defence responses, such as kauralexin production by the diterpenoid biosynthesis pathway. The co-expression module that correlated best with resistance (TQ-r; 1498 genes) was enriched for genes with trans-eQTLs in hotspots coinciding with GLS resistance QTLs on chromosome 9. Jasmonate responses were implicated in resistance to GLS through co-expression of COI1 and enrichment of genes with the Gene Ontology term 'cullin-RING ubiquitin ligase complex' in the TQ-r module. Consistent with this, JAZ repressor expression was highly correlated with the severity of GLS disease in the GY-s susceptibility network.

IMA Genome-F 6: Draft genome sequences of Armillaria fuscipes, Ceratocystiopsis minuta, Ceratocystis adiposa, Endoconidiophora laricicola, E. polonica and Penicillium freii DAOMC 242723.

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.

The Development of Computational Biology in South Africa: Successes Achieved and Lessons Learnt.

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.

H3ABioNet, a sustainable pan-African bioinformatics network for human heredity and health in Africa.

The application of genomics technologies to medicine and biomedical research is increasing in popularity, made possible by new high-throughput genotyping and sequencing technologies and improved data analysis capabilities. Some of the greatest genetic diversity among humans, animals, plants, and microbiota occurs in Africa, yet genomic research outputs from the continent are limited. The Human Heredity and Health in Africa (H3Africa) initiative was established to drive the development of genomic research for human health in Africa, and through recognition of the critical role of bioinformatics in this process, spurred the establishment of H3ABioNet, a pan-African bioinformatics network for H3Africa. The limitations in bioinformatics capacity on the continent have been a major contributory factor to the lack of notable outputs in high-throughput biology research. Although pockets of high-quality bioinformatics teams have existed previously, the majority of research institutions lack experienced faculty who can train and supervise bioinformatics students. H3ABioNet aims to address this dire need, specifically in the area of human genetics and genomics, but knock-on effects are ensuring this extends to other areas of bioinformatics. Here, we describe the emergence of genomics research and the development of bioinformatics in Africa through H3ABioNet.