A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing.

As whole-genome sequencing for cancer genome analysis becomes a clinical tool, a full understanding of the variables affecting sequencing analysis output is required. Here using tumour-normal sample pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, we conduct a benchmarking exercise within the context of the International Cancer Genome Consortium. We compare sequencing methods, analysis pipelines and validation methods. We show that using PCR-free methods and increasing sequencing depth to ∼ 100 × shows benefits, as long as the tumour:control coverage ratio remains balanced. We observe widely varying mutation call rates and low concordance among analysis pipelines, reflecting the artefact-prone nature of the raw data and lack of standards for dealing with the artefacts. However, we show that, using the benchmark mutation set we have created, many issues are in fact easy to remedy and have an immediate positive impact on mutation detection accuracy.We thank the DKFZ Genomics and Proteomics Core Facility and the OICR Genome Technologies Platform for provision of sequencing services. Financial support was provided by the consortium projects READNA under grant agreement FP7 Health-F4-2008-201418, ESGI under grant agreement 262055, GEUVADIS under grant agreement 261123 of the European Commission Framework Programme 7, ICGC-CLL through the Spanish Ministry of Science and Innovation (MICINN), the Instituto de Salud Carlos III (ISCIII) and the Generalitat de Catalunya. Additional financial support was provided by the PedBrain Tumor Project contributing to the International Cancer Genome Consortium, funded by German Cancer Aid (109252) and by the German Federal Ministry of Education and Research (BMBF, grants #01KU1201A, MedSys #0315416C and NGFNplus #01GS0883; the Ontario Institute for Cancer Research to PCB and JDM through funding provided by the Government of Ontario, Ministry of Research and Innovation; Genome Canada; the Canada Foundation for Innovation and Prostate Cancer Canada with funding from the Movember Foundation (PCB). PCB was also supported by a Terry Fox Research Institute New Investigator Award, a CIHR New Investigator Award and a Genome Canada Large-Scale Applied Project Contract. The Synergie Lyon Cancer platform has received support from the French National Institute of Cancer (INCa) and from the ABS4NGS ANR project (ANR-11-BINF-0001-06). The ICGC RIKEN study was supported partially by RIKEN President’s Fund 2011, and the supercomputing resource for the RIKEN study was provided by the Human Genome Center, University of Tokyo. MDE, LB, AGL and CLA were supported by Cancer Research UK, the University of Cambridge and Hutchison-Whampoa Limited. SD is supported by the Torres Quevedo subprogram (MI CINN) under grant agreement PTQ-12-05391. EH is supported by the Research Council of Norway under grant agreements 221580 and 218241 and by the Norwegian Cancer Society under grant agreement 71220-PR-2006-0433. Very special thanks go to Jennifer Jennings for administrating the activity of the ICGC Verification Working Group and Anna Borrell for administrative support.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms1000

Target protection as a key antibiotic resistance mechanism

Antibiotic resistance is mediated through several distinct mechanisms, most of which are relatively well understood and the clinical importance of which has long been recognized. Until very recently, neither of these statements was readily applicable to the class of resistance mechanism known as target protection, a phenomenon whereby a resistance protein physically associates with an antibiotic target to rescue it from antibiotic-mediated inhibition. In this Review, we summarize recent progress in understanding the nature and importance of target protection. In particular, we describe the molecular basis of the known target protection systems, emphasizing that target protection does not involve a single, uniform mechanism but is instead brought about in several mechanistically distinct ways

Translation and Co-Translational Membrane Engagement of Plastid-Encoded Chlorophyll-Binding Proteins are not Influenced by Chlorophyll Availability in Maize

Chlorophyll is an indispensable constituent of the photosynthetic machinery in green organisms. Bound by apoproteins of photosystems I and II, chlorophyll performs light-harvesting and charge separation. Due to the phototoxic nature of free chlorophyll and its precursors, chlorophyll synthesis is regulated to comply with the availability of nascent chlorophyll-binding apoproteins. Conversely, the synthesis and co-translational insertion of such proteins into the thylakoid membrane have been suggested to be influenced by chlorophyll availability. In this study, we addressed these hypotheses by using ribosome profiling to examine the synthesis and membrane targeting of chlorophyll-binding apoproteins in chlorophyll-deficient chlH maize mutants (Zm-chlH). ChlH encodes the H subunit of the magnesium chelatase (also known as GUN5), which catalyzes the first committed step in chlorophyll synthesis. Our results show that the number and distribution of ribosomes on plastid mRNAs encoding chlorophyll-binding apoproteins are not substantially altered in Zm-chlH mutants, suggesting that chlorophyll has no impact on ribosome dynamics. Additionally, a Zm-chlH mutation does not change the amino acid position at which nascent chlorophyll-binding apoproteins engage the thylakoid membrane, nor the efficiency with which membrane-engagement occurs. Together, these results provide evidence that chlorophyll availability does not selectively activate the translation of plastid mRNAs encoding chlorophyll apoproteins. Our results imply that co- or post-translational proteolysis of apoproteins is the primary mechanism that adjusts apoprotein abundance to chlorophyll availability in plants

A Guide to the Chloroplast Transcriptome Analysis Using RNA-Seq

Since its first use in plants in 2007, high-throughput RNA sequencing (RNA-Seq) has generated a vast amount of data for both model and nonmodel species. Organellar transcriptomes, however, are virtually always overlooked at the data analysis step. We therefore developed ChloroSeq, a bioinformatic pipeline aimed at facilitating the systematic analysis of chloroplast RNA metabolism, and we provide here a step-by--step user's manual. Following the alignment of quality-controlled data to the genome of interest, ChloroSeq measures genome expression level along with splicing and RNA editing efficiencies. When used in combination with the Tuxedo suite (TopHat and Cufflinks), ChloroSeq allows the simultaneous analysis of organellar and nuclear transcriptomes, opening the way to a better understanding of nucleus-organelle cross talk. We also describe the use of R commands to produce publication-quality figures based on ChloroSeq outputs. The effectiveness of the pipeline is illustrated through analysis of an RNA-Seq dataset covering the transition from growth to maturation to senescence of Arabidopsis thaliana leaves