It was a zoological sensation when a living specimen of the coelacanth was first discovered in 1938, as this lineage of lobe-finned fish was thought to have gone extinct 70 million years ago. The modern coelacanth looks remarkably similar to many of its ancient relatives, and its evolutionary proximity to our own fish ancestors provides a glimpse of the fish that first walked on land. Here we report the genome sequence of the African coelacanth, Latimeria chalumnae. Through a phylogenomic analysis, we conclude that the lungfish, and not the coelacanth, is the closest living relative of tetrapods. Coelacanth protein-coding genes are significantly more slowly evolving than those of tetrapods, unlike other genomic features . Analyses of changes in genes and regulatory elements during the vertebrate adaptation to land highlight genes involved in immunity, nitrogen excretion and the development of fins, tail, ear, eye, brain, and olfaction. Functional assays of enhancers involved in the fin-to-limb transition and in the emergence of extra-embryonic tissues demonstrate the importance of the coelacanth genome as a blueprint for understanding tetrapod evolution.

H3Africa is developing capacity for health-related genomics research in Africa

Background: The genome of Mycobacterium tuberculosis H37Rv has five copies of a cluster of genes known as the ESAT-6 loci. These clusters contain members of the CFP-10 (lhp) and ESAT-6 (esat-6) gene families (encoding secreted T-cell antigens that lack detectable secretion signals) as well as genes encoding secreted, cell-wall-associated subtilisin-like serine proteases, putative ABC transporters, ATP-binding proteins and other membrane-associated proteins. These membraneassociated and energy-providing proteins may function to secrete members of the ESAT-6 and CFP-10 protein families, and the proteases may be involved in processing the secreted peptide.

The rapid advancement of molecular tools in the past 15 years has allowed for the retrospective discovery of several new respiratory viruses as well as the characterization of novel emergent strains. The inability to characterize the etiological origins of respiratory conditions, particularly in children, led several researchers to pursue the discovery of the underlying etiology of disease. In 2001, this led to the discovery of human metapneumovirus (hMPV) and soon following that the outbreak of Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) promoted an increased interest in coronavirology and the latter discovery of human coronavirus (HCoV) NL63 and HCoV-HKU1. Human bocavirus, with its four separate lineages, discovered in 2005, has been linked to acute respiratory tract infections and gastrointestinal complications. Middle East Respiratory Syndrome coronavirus (MERS-CoV) represents the most recent outbreak of a completely novel respiratory virus, which occurred in Saudi Arabia in 2012 and presents a significant threat to human health. This review will detail the most current clinical and epidemiological findings to all respiratory viruses discovered since 2001.

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.

Human coronaviruses represent a significant disease burden; however, there is currently no antiviral strategy to combat infection. The outbreak of severe acute respiratory syndrome (SARS) in 2003 and Middle East respiratory syndrome (MERS) less than 10 years later demonstrates the potential of coronaviruses to cross species boundaries and further highlights the importance of identifying novel lead compounds with broad spectrum activity. The coronavirus 3CLpro provides a highly validated drug target and as there is a high degree of sequence homology and conservation in main chain architecture the design of broad spectrum inhibitors is viable. The ZINC drugs-now library was screened in a consensus high-throughput pharmacophore modeling and molecular docking approach by Vina, Glide, GOLD and MM-GBSA. Molecular dynamics further confirmed results obtained from structure-based techniques. A highly defined hit-list of 19 compounds was identified by the structure-based drug design methodologies. As these compounds were extensively validated by a consensus approach and by molecular dynamics, the likelihood that at least one of these compounds is bioactive is excellent. Additionally, the compounds segregate into 15 significantly dissimilar (p < 0.05) clusters based on shape and features, which represent valuable scaffolds that can be used as a basis for future anti-coronaviral inhibitor discovery experiments. Importantly though, the enriched subset of 19 compounds identified from the larger library has to be validated experimentally.

BackgroundIn the post-genomic era, a central and overarching question in the analysis of protein-protein interaction networks continues to be whether biological characteristics and functions of proteins such as lethality, physiological malfunctions and malignancy are intimately linked to the topological role proteins play in the network as a mathematical structure. One of the key features that have implicitly been presumed is the existence of hubs, highly connected proteins considered to play a crucial role in biological networks. We explore the structure of protein interaction networks of a number of organisms as metric spaces and show that hubs are non randomly positioned and, from a distance point of view, centrally located.ResultsBy analysing how the human functional protein interaction network, the human signalling network, Saccharomyces cerevisiae, Arabidopsis thaliana and Escherichia coli protein-protein interaction networks from various databases are distributed as metric spaces, we found that proteins interact radially through a central node, high degree proteins coagulate in the centre of the network, and those far away from the centre have low degree. We further found that the distribution of proteins from the centre is in some hierarchy of importance and has biological significance.ConclusionsWe conclude that structurally, protein interaction networks are mathematical entities that share properties between organisms but not necessarily with other networks that follow power-law. We therefore conclude that (i) if there are hubs defined by degree, they are not distributed randomly; (ii) zones closest to the centre of the network are enriched for critically important proteins and are also functionally very specialised for specific 'house keeping’ functions; (iii) proteins closest to the network centre are functionally less dispensable and may present good targets for therapy development; and (iv) network biology requires its own network theory modelled on actual biological evidence and that simply adopting theories from the social sciences may be misleading.

Next-generation sequencing (NGS) of whole genomes and exomes is a powerful tool in biomedical research and clinical diagnostics. However, the vast amount of data produced by NGS introduces new challenges and opportunities, many of which require novel computational and theoretical approaches when it comes to identifying the causal variant(s) for a disease of interest. While workflows and associated software to process raw data and produce high-confidence variant calls have significantly improved, filtering tens of thousands of candidates to identify a subset relevant to a specific study is still a complex exercise best left to bioinformaticists. However, as this prioritization procedure requires biological/biomedical reasoning, biologists and clinicians are increasingly motivated to handle the task themselves. Here, we describe a set of guidelines, tools, and online resources that can be used to identify functional variants from whole-genome and whole-exome variant calls and then prioritize these variants with potential associations to phenotypes of interest. Insights gained from a recently published analysis of protein-coding gene variation in >60,000 humans by the Exome Aggregation Consortium (ExAC) are also taken into account.