DNA-Dependent Binding of Nargenicin to DnaE1 Inhibits Replication in Mycobacterium tuberculosis.

Natural products provide a rich source of potential antimicrobials for treating infectious diseases for which drug resistance has emerged. Foremost among these diseases is tuberculosis. Assessment of the antimycobacterial activity of nargenicin, a natural product that targets the replicative DNA polymerase of Staphylococcus aureus, revealed that it is a bactericidal genotoxin that induces a DNA damage response in Mycobacterium tuberculosis (Mtb) and inhibits growth by blocking the replicative DNA polymerase, DnaE1. Cryo-electron microscopy revealed that binding of nargenicin to Mtb DnaE1 requires the DNA substrate such that nargenicin is wedged between the terminal base pair and the polymerase and occupies the position of both the incoming nucleotide and templating base. Comparative analysis across three bacterial species suggests that the activity of nargenicin is partly attributable to the DNA binding affinity of the replicative polymerase. This work has laid the foundation for target-led drug discovery efforts focused on Mtb DnaE1.

TBDBT: A TB DataBase Template for collection of harmonized TB clinical research data in REDCap, facilitating data standardisation for inter-study comparison and meta-analyses.

Clinical tuberculosis research, both within research groups and across research ecosystems, is often undertaken in isolation using bespoke data collection platforms and applying differing data conventions. This failure to harmonise clinical phenotype data or apply standardised data collection and storage standards in turn limits the opportunity to undertake meta-analyses using data generated across multiple research projects for the same research domain. We have developed the Tuberculosis DataBase Template (TBDBT), a template for the well-supported, free and commonly deployed clinical databasing platform, REDCap. This template can be used to set up a new tuberculosis research database with a built-in set of standardised data conventions, to ensure standardised data capture across research projects and programs. A modular design enables researchers to implement only the modules of the database template that are appropriate for their particular study. The template includes core modules for informed consent data, participant demographics, clinical symptoms and presentation, diagnostic imaging and laboratory tests. Optional modules have been designed for visit scheduling and calendar functionality, clinical trial randomisation, study logistics and operations, and pharmacokinetic data. Additional fields can be added as needed. This REDCap template can facilitate collection of high-quality data for tuberculosis research, providing a tool to ensure better data harmonisation, analysis and meta-analysis.

Including Digital Sequence Data in the Nagoya Protocol Can Promote Data Sharing.

The Nagoya Protocol (NP), a legal framework under the Convention on Biological Diversity (CBD), formalises fair and equitable sharing of benefits arising from biological diversity. It encompasses biological samples and associated indigenous knowledge, with equitable return of benefits to those providing samples. Recent proposals that the use of digital sequence information (DSI) derived from samples should also require benefit-sharing under the NP have raised concerns that this might hamper research progress. Here, we propose that formalised benefit-sharing for biological data use can increase willingness to participate in research and share data, by ensuring equitable collaboration between sample providers and researchers, and preventing exploitative practices. Three case studies demonstrate how equitable benefit-sharing agreements might build long-term collaborations, furthering research for global benefits.

Equitable Science Requires Collegial Debate.

We welcome an ongoing debate about benefit-sharing related to the use of digital sequence information (DSI) and how to ensure global equity in the use of biological samples, including those donated by human beings.

GenGraph: a python module for the simple generation and manipulation of genome graphs.

BACKGROUND: As sequencing technology improves, the concept of a single reference genome is becoming increasingly restricting. In the case of Mycobacterium tuberculosis, one must often choose between using a genome that is closely related to the isolate, or one that is annotated in detail. One promising solution to this problem is through the graph based representation of collections of genomes as a single genome graph. Though there are currently a handful of tools that can create genome graphs and have demonstrated the advantages of this new paradigm, there still exists a need for flexible tools that can be used by researchers to overcome challenges in genomics studies. RESULTS: We present GenGraph, a Python toolkit and accompanying modules that use existing multiple sequence alignment tools to create genome graphs. Python is one of the most popular coding languages for the biological sciences, and by providing these tools, GenGraph makes it easier to experiment and develop new tools that utilise genome graphs. The conceptual model used is highly intuitive, and as much as possible the graph structure represents the biological relationship between the genomes. This design means that users will quickly be able to start creating genome graphs and using them in their own projects. We outline the methods used in the generation of the graphs, and give some examples of how the created graphs may be used. GenGraph utilises existing file formats and methods in the generation of these graphs, allowing graphs to be visualised and imported with widely used applications, including Cytoscape, R, and Java Script. CONCLUSIONS: GenGraph provides a set of tools for generating graph based representations of sets of sequences with a simple conceptual model, written in the widely used coding language Python, and publicly available on Github.

EPHA2 sequence variants are associated with susceptibility to Kaposi's sarcoma-associated herpesvirus infection and Kaposi's sarcoma prevalence in HIV-infected patients.

BACKGROUND: To determine if variations exist in the KSHV host receptor EPHA2's coding region that affect KSHV infectivity and/or KS prevalence among South African HIV-infected patients. METHODS: A retrospective candidate gene association study was performed on 150 patients which were randomly selected from a total of 756 HIV-infected patients and grouped according to their KS status and KSHV serodiagnosis; namely group 1: KS+/KSHV+; group 2: KS-/KSHV+; group 3: KS-/KSHV-. Peripheral blood DNA was used to extract DNA and PCR amplify and sequence the entire EPHA2 coding region, which was compared to the NCBI reference through multiple alignment. RESULTS: 100% (95% CI 92.9-100%) of the KS positive patients, and 31.6% (95% CI 28.3-35.1%) of the KS negative patients were found to be KSHV seropositive. Aggregate variation across the entire EPHA2 coding region identified an association with KS (OR = 6.6 (95% CI 2.8, 15.9), p = 2.2 × 10-5). This was primarily driven by variation in the functionally important protein tyrosine kinase domain (Pkinase-Tyr; OR = 4.9 (95% CI 1.9, 12.4), p = 0.001) and the sterile-α-motif (SAM; OR = 13.8 (95% CI 1.7, 111.6), p = 0.014). Mutation analysis revealed two novel, non-synonymous heterozygous variants (c.2254 T > C: OR undefined, adj. p = 0.02; and c.2990 G > T: OR undefined, adj. p = 0.04) in Pkinase-Tyr and SAM, respectively, to be statistically associated with KS; and a novel heterozygous transition (c.2727C > T: OR = 6.4 (95% CI 1.4, 28.4), adj. p = 0.03) in Pkinase-Tyr to be statistically associated with KSHV. CONCLUSIONS: Variations in the KSHV entry receptor gene EPHA2 affected susceptibility to KSHV infection and KS development in a South African HIV-infected patient cohort.

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.

Proteogenomic Analysis of Mycobacterium smegmatis Using High Resolution Mass Spectrometry.

Biochemical evidence is vital for accurate genome annotation. The integration of experimental data collected at the proteome level using high resolution mass spectrometry allows for improvements in genome annotation by providing evidence for novel gene models, while validating or modifying others. Here, we report the results of a proteogenomic analysis of a reference strain of Mycobacterium smegmatis (mc(2)155), a fast growing model organism for the pathogenic Mycobacterium tuberculosis-the causative agent for Tuberculosis. By integrating high throughput LC/MS/MS proteomic data with genomic six frame translation and ab initio gene prediction databases, a total of 2887 ORFs were identified, including 2810 ORFs annotated to a Reference protein, and 63 ORFs not previously annotated to a Reference protein. Further, the translational start site (TSS) was validated for 558 Reference proteome gene models, while upstream translational evidence was identified for 81. In addition, N-terminus derived peptide identifications allowed for downstream TSS modification of a further 24 gene models. We validated the existence of six previously described interrupted coding sequences at the peptide level, and provide evidence for four novel frameshift positions. Analysis of peptide posterior error probability (PEP) scores indicates high-confidence novel peptide identifications and shows that the genome of M. smegmatis mc(2)155 is not yet fully annotated. Data are available via ProteomeXchange with identifier PXD003500.