Craniofacial, dental, and molecular features of Pyle disease in a South African child.

INTRODUCTION: Pyle Disease (PD), or familial metaphyseal dysplasia [OMIM 265900], is a rare autosomal recessive condition leading to widened metaphyses of long bones and cortical bone thinning and genu valgum. We detail the oro-dental and molecular findings in a South African patient with PD. METHODS: The patient underwent clinical, radiographic and molecular examinations. An exfoliated tooth was analysed using scanning electron microscopy and was compared to a control tooth. RESULTS: The patient presented with marked Erlenmeyer-flask deformity (EFD) of the long bones and several Wormian bones. His dental development was delayed by approximately three years. The permanent molars were mesotaurodontic. The bones, including the jaws and cervical vertebrae, showed osteoporotic changes. The lamina dura was absent, and the neck of the condyle lacked normal constrictions. Ionic component analysis of the primary incisors found an absence of magnesium. Sanger sequencing revealed a novel putative pathogenic variant in intron 5 of SFRP4 (c.855+4delAGTA) in a homozygous state. CONCLUSION: This study has reported for the first time the implication of a mutation in the SFRP4 gene in an African patient presenting with PD and highlights the need for dental practitioners to be made aware of the features and management implications of PD.

Implementing Artificial Intelligence and Digital Health in Resource-Limited Settings? Top 10 Lessons We Learned in Congenital Heart Defects and Cardiology.

Artificial intelligence (AI) is one of the key drivers of digital health. Digital health and AI applications in medicine and biology are emerging worldwide, not only in resource-rich but also resource-limited regions. AI predates to the mid-20th century, but the current wave of AI builds in part on machine learning (ML), big data, and algorithms that can learn from massive amounts of online user data from patients or healthy persons. There are lessons to be learned from AI applications in different medical specialties and across developed and resource-limited contexts. A case in point is congenital heart defects (CHDs) that continue to plague sub-Saharan Africa, which calls for innovative approaches to improve risk prediction and performance of the available diagnostics. Beyond CHDs, AI in cardiology is a promising context as well. The current suite of digital health applications in CHD and cardiology include complementary technologies such as neural networks, ML, natural language processing and deep learning, not to mention embedded digital sensors. Algorithms that build on these advances are beginning to complement traditional medical expertise while inviting us to redefine the concepts and definitions of expertise in molecular diagnostics and precision medicine. We examine and share here the lessons learned in current attempts to implement AI and digital health in CHD for precision risk prediction and diagnosis in resource-limited settings. These top 10 lessons on AI and digital health summarized in this expert review are relevant broadly beyond CHD in cardiology and medical innovations. As with AI itself that calls for systems approaches to data capture, analysis, and interpretation, both developed and developing countries can usefully learn from their respective experiences as digital health continues to evolve worldwide.

Dissecting in silico Mutation Prediction of Variants in African Genomes: Challenges and Perspectives.

Genomic medicine is set to drastically improve clinical care globally due to high throughput technologies which enable speedy in silico detection and analysis of clinically relevant mutations. However, the variability in the in silico prediction methods and categorization of functionally relevant genetic variants can pose specific challenges in some populations. In silico mutation prediction tools could lead to high rates of false positive/negative results, particularly in African genomes that harbor the highest genetic diversity and that are disproportionately underrepresented in public databases and reference panels. These issues are particularly relevant with the recent increase in initiatives, such as the Human Heredity and Health (H3Africa), that are generating huge amounts of genomic sequence data in the absence of policies to guide genomic researchers to return results of variants in so-called actionable genes to research participants. This report (i) provides an inventory of publicly available Whole Exome/Genome data from Africa which could help improve reference panels and explore the frequency of pathogenic variants in actionable genes and related challenges, (ii) reviews available in silico prediction mutation tools and the criteria for categorization of pathogenicity of novel variants, and (iii) proposes recommendations for analyzing pathogenic variants in African genomes for their use in research and clinical practice. In conclusion, this work proposes criteria to define mutation pathogenicity and actionability in human genetic research and clinical practice in Africa and recommends setting up an African expert panel to oversee the proposed criteria.

Noonan Syndrome in South Africa: Clinical and Molecular Profiles.

Noonan Syndrome (NS) is a common autosomal dominant multisystem disorder, caused by mutations in more than 10 genes in the Ras/MAPK signaling pathway. Differential mutation frequencies are observed across populations. Clinical expressions of NS are highly variable and include short stature, distinctive craniofacial dysmorphism, cardiovascular abnormalities, and developmental delay. Little is known about phenotypic specificities and molecular characteristics of NS in Africa. The present study has investigated patients with NS in Cape Town (South Africa). Clinical features were carefully documented in a total of 26 patients. Targeted Next-Generation Sequencing (NGS) was performed on 16 unrelated probands, using a multigene panel comprising 14 genes: PTPN11, SOS1, RIT1, A2ML1, BRAF, CBL, HRAS, KRAS, MAP2K1, MAP2K2, NRAS, RAF1, SHOC2, and SPRED1. The median age at diagnosis was 4.5 years (range: 1 month-51 years). Individuals of mixed-race ancestry were most represented (53.8%), followed by black Africans (30.8%). Our cohort revealed a lower frequency of pulmonary valve stenosis (34.6%) and a less severe developmental milestones phenotype. Molecular analysis found variants predicted to be pathogenic in 5 / 16 cases (31.2%). Among these mutations, two were previously reported: MAP2K1-c.389A>G (p.Tyr130Cys) and PTPN11 - c.1510A>G (p.Met504Val); three are novel: CBL-c.2520T>G (p.Cys840Trp), PTPN11- c.1496C>T (p.Ser499Phe), and MAP2K1- c.200A>C (p.Asp67Ala). Molecular dynamic simulations indicated that novel variants identified impact the stability and flexibility of their corresponding proteins. Genotype-phenotype correlations showed that clinical features of NS were more typical in patients with variants in MAP2K1. This first application of targeted NGS for the molecular diagnosis of NS in South Africans suggests that, while there is no major phenotypic difference compared to other populations, the distribution of genetic variants in NS in South Africans may be different.

Fluctuation matching approach for elastic network model and structure-based model of biomacromolecules.

Elastic network models (ENMs) based on simple harmonic potential energy function have been proven over the last decade to be reliable computational models for understanding the intrinsic dynamics of biomacromolecules. In the original ENMs, the spring constants for different contact pairs are assumed to be identical, while there are a number of recent developments to determine non-uniform spring constants from atomistic force fields or experimental information. In particular, the fluctuation matching approaches in coarse-grained modeling can be applied to build more realistic heterogeneous ENMs, using information from an atomistic force field or experimental B-factors. The same type of approaches is further implemented to parameterize heterogeneous structure-based models, which can be considered as a natural extension of ENMs in terms of the potential energy function. In this review, we give an overview of different fluctuation matching methods adopted for ENMs and structure-based models, including an improved formulation and algorithm based on the relative entropy scheme.