(13) C magnetic resonance spectroscopy measurements with hyperpolarized [1-(13) C] pyruvate can be used to detect the expression of transgenic pyruvate decarboxylase activity in vivo.

PURPOSE: Dissolution dynamic nuclear polarization can increase the sensitivity of the (13) C magnetic resonance spectroscopy experiment by at least four orders of magnitude and offers a novel approach to the development of MRI gene reporters based on enzymes that metabolize (13) C-labeled tracers. We describe here a gene reporter based on the enzyme pyruvate decarboxylase (EC 4.1.1.1), which catalyzes the decarboxylation of pyruvate to produce acetaldehyde and carbon dioxide. METHODS: Pyruvate decarboxylase from Zymomonas mobilis (zmPDC) and a mutant that lacked enzyme activity were expressed using an inducible promoter in human embryonic kidney (HEK293T) cells. Enzyme activity was measured in the cells and in xenografts derived from the cells using (13) C MRS measurements of the conversion of hyperpolarized [1-(13) C] pyruvate to H(13) CO3-. RESULTS: Induction of zmPDC expression in the cells and in the xenografts derived from them resulted in an approximately two-fold increase in the H(13) CO3-/[1-(13) C] pyruvate signal ratio following intravenous injection of hyperpolarized [1-(13) C] pyruvate. CONCLUSION: We have demonstrated the feasibility of using zmPDC as an in vivo reporter gene for use with hyperpolarized (13) C MRS. Magn Reson Med 76:391-401, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.PD was in receipt of a studentship funded by CRUK and S.-S.T. a Yousef Jameel studentship. TBR was in receipt of an Intra-European Marie Curie (FP7-PEOPLE- 2009-IEF, Imaging Lymphoma) and Long-term EMBO (EMBO-ALT-1145-2009) fellowships and E.M.S. and I.M.R were recipients of fellowships from the European Union Seventh Framework Programme (FP7/2007-2013) under the Marie Curie Initial Training Network METAFLUX (project number 264780). E.M.S. also acknowledges the educational support of Programme for Advanced Medical Education from Calouste Gulbenkian Foundation, Champalimaud Foundation, Ministerio de Saude and Fundacao para a Ciencia e Tecnologia, Portugal. The work was supported by a CRUK Programme Grant (17242) to KMB. The polarizer and related materials were provided by GE-Healthcare.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/mrm.2587

Comparison of dynamic monitoring strategies based on CD4 cell counts in virally suppressed, HIV-positive individuals on combination antiretroviral therapy in high-income countries: a prospective, observational study

BACKGROUND: Clinical guidelines vary with respect to the optimal monitoring frequency of HIV-positive individuals. We compared dynamic monitoring strategies based on time-varying CD4 cell counts in virologically suppressed HIV-positive individuals. METHODS: In this observational study, we used data from prospective studies of HIV-positive individuals in Europe (France, Greece, the Netherlands, Spain, Switzerland, and the UK) and North and South America (Brazil, Canada, and the USA) in The HIV-CAUSAL Collaboration and The Centers for AIDS Research Network of Integrated Clinical Systems. We compared three monitoring strategies that differ in the threshold used to measure CD4 cell count and HIV RNA viral load every 3–6 months (when below the threshold) or every 9–12 months (when above the threshold). The strategies were defined by the threshold CD4 counts of 200 cells per μL, 350 cells per μL, and 500 cells per μL. Using inverse probability weighting to adjust for baseline and time-varying confounders, we estimated hazard ratios (HRs) of death and of AIDS-defining illness or death, risk ratios of virological failure, and mean differences in CD4 cell count. FINDINGS: 47 635 individuals initiated an antiretroviral therapy regimen between Jan 1, 2000, and Jan 9, 2015, and met the eligibility criteria for inclusion in our study. During follow-up, CD4 cell count was measured on average every 4·0 months and viral load every 3·8 months. 464 individuals died (107 in threshold 200 strategy, 157 in threshold 350, and 200 in threshold 500) and 1091 had AIDS-defining illnesses or died (267 in threshold 200 strategy, 365 in threshold 350, and 459 in threshold 500). Compared with threshold 500, the mortality HR was 1·05 (95% CI 0·86–1·29) for threshold 200 and 1·02 (0·91·1·14) for threshold 350. Corresponding estimates for death or AIDS-defining illness were 1·08 (0·95–1·22) for threshold 200 and 1·03 (0·96–1·12) for threshold 350. Compared with threshold 500, the 24 month risk ratios of virological failure (viral load more than 200 copies per mL) were 2·01 (1·17–3·43) for threshold 200 and 1·24 (0·89–1·73) for threshold 350, and 24 month mean CD4 cell count differences were 0·4 (−25·5 to 26·3) cells per μL for threshold 200 and −3·5 (−16·0 to 8·9) cells per μL for threshold 350. INTERPRETATION: Decreasing monitoring to annually when CD4 count is higher than 200 cells per μL compared with higher than 500 cells per μL does not worsen the short-term clinical and immunological outcomes of virally suppressed HIV-positive individuals. However, more frequent virological monitoring might be necessary to reduce the risk of virological failure. Further follow-up studies are needed to establish the long-term safety of these strategies. FUNDING National Institutes of Health

Family Businesses and Adaptation: A Dynamic Capabilities Approach

The main objective of this research was to propose a framework centred on the dynamic capabilities approach, and to be applied in the context of family businesses’ adaption to their changing business environment. Data were gathered through interviews with ten FBs operating in Western Australia. Based on the findings, the clusters of activities, sensing, seizing, and transforming emerged as key factors for firms’ adaptation, and were reinforced by firms’ open culture, signature processes, idiosyncratic knowledge, and valuable, rare, inimitable and non-substitutable attributes. Thus, the usefulness of the proposed framework was confirmed. Implications and future research opportunities are presented. © 2018, The Author(s)

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe