ALBERT

Description: Species migrations in response to climate change have already been observed in many taxonomic groups worldwide. However, it remains uncertain if species will be able to keep pace with future climate change. Keeping pace will be especially challenging for tropical lowland rainforests due to their high velocities of climate change combined with high rates of deforestation, which may eliminate potential climate analogs and/or increase the effective distances between analogs by blocking species movements. Here we calculate the distances between current and future climate analogs under various climate change and deforestation scenarios. Under even the most sanguine of climate change models (IPSL_CM4, A1b emissions scenario), we find that the median distance between areas in the Amazon rainforest and their closest future (2050) climate analog as predicted based on just temperature changes alone is nearly 300 km. If we include precipitation, the median distance increases by over 50% to 〉475 km. Since deforestation is generally concentrated in the hottest and driest portions of the Amazon, we predict that habitat loss will have little direct impact on distances between climate analogs. If, however, deforested areas also act as a barrier to species movements, nearly 30 or 55% of the Amazon will effectively have no climate analogs anywhere in tropical South America under projections of reduced or Business-As-Usual deforestation, respectively. These “disappearing climates” will be concentrated primarily in the southeastern Amazon. Consequently, we predict that several Amazonian ecoregions will have no areas with future climate analogs, greatly increasing the vulnerability of any populations or species specialized on these conditions. These results highlight the importance of including multiple climatic factors and human land-use in predicting the effects of climate change, as well as the daunting challenges that Amazonian diversity faces in the near future. © 2012 Blackwell Publishing Ltd

Description: Previous studies have shown the great potential, but also the great challenges, in handling slim reactors often used for polymerization reactions. Experiments and simulations were carried out in reactors with aspect-to-diameter ratios of up to 5, to test and to evaluate the mixing and dispersion efficiency for liquid-liquid systems of single- and multiple-stage impellers. Therefore, power consumption, mixing time and minimum dispersion speed were determined for five different stirrer types under turbulent conditions. It was found that the dimensionless mixing time is highly sensitive to the configuration of the impellers, with almost no dependency on the turbulent power number. Another focus was the analysis of the effect of the baffles. The influence of the baffle length in slim reactors on the mixing time and the macroscopic flow field was determined. Experiments and simulations were carried out in slim reactors to test and to evaluate the mixing and dispersion efficiency of single- and multiple-stage impellers for liquid-liquid systems. Power consumption, mixing time and minimum dispersion speed were determined for five different stirrer types under turbulent conditions.

Description: Modulation of apoptosis sensitivity through the interplay with autophagic and proteasomal degradation pathways Cell Death and Disease 5, e1011 (January 2014). doi:10.1038/cddis.2013.520 Authors: M E Delgado, L Dyck, M A Laussmann & M Rehm

Description: Article Dendritic cells (DC) are known to promote cancer progression by suppressing antitumor immunity. Here, Rehm et al . describe a mechanism whereby lymphoma cells induce C/EBPβ activation in DCs, which in turn secrete cytokines that support the proliferation and survival of lymphoma cells. Nature Communications doi: 10.1038/ncomms6057 Authors: Armin Rehm, Marcel Gätjen, Kerstin Gerlach, Florian Scholz, Angela Mensen, Marleen Gloger, Kristina Heinig, Björn Lamprecht, Stephan Mathas, Valérie Bégay, Achim Leutz, Martin Lipp, Bernd Dörken, Uta E. Höpken

Description: Nature Climate Change 4 405 doi: 10.1038/nclimate2207

Description: Despite the large evolutionary distances between metazoan species, they can show remarkable commonalities in their biology, and this has helped to establish fly and worm as model organisms for human biology. Although studies of individual elements and factors have explored similarities in gene regulation, a large-scale comparative analysis of basic principles of transcriptional regulatory features is lacking. Here we map the genome-wide binding locations of 165 human, 93 worm and 52 fly transcription regulatory factors, generating a total of 1,019 data sets from diverse cell types, developmental stages, or conditions in the three species, of which 498 (48.9%) are presented here for the first time. We find that structural properties of regulatory networks are remarkably conserved and that orthologous regulatory factor families recognize similar binding motifs in vivo and show some similar co-associations. Our results suggest that gene-regulatory properties previously observed for individual factors are general principles of metazoan regulation that are remarkably well-preserved despite extensive functional divergence of individual network connections. The comparative maps of regulatory circuitry provided here will drive an improved understanding of the regulatory underpinnings of model organism biology and how these relate to human biology, development and disease.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4336544/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4336544/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Boyle, Alan P -- Araya, Carlos L -- Brdlik, Cathleen -- Cayting, Philip -- Cheng, Chao -- Cheng, Yong -- Gardner, Kathryn -- Hillier, LaDeana W -- Janette, Judith -- Jiang, Lixia -- Kasper, Dionna -- Kawli, Trupti -- Kheradpour, Pouya -- Kundaje, Anshul -- Li, Jingyi Jessica -- Ma, Lijia -- Niu, Wei -- Rehm, E Jay -- Rozowsky, Joel -- Slattery, Matthew -- Spokony, Rebecca -- Terrell, Robert -- Vafeados, Dionne -- Wang, Daifeng -- Weisdepp, Peter -- Wu, Yi-Chieh -- Xie, Dan -- Yan, Koon-Kiu -- Feingold, Elise A -- Good, Peter J -- Pazin, Michael J -- Huang, Haiyan -- Bickel, Peter J -- Brenner, Steven E -- Reinke, Valerie -- Waterston, Robert H -- Gerstein, Mark -- White, Kevin P -- Kellis, Manolis -- Snyder, Michael -- F32GM101778/GM/NIGMS NIH HHS/ -- P50GM081892/GM/NIGMS NIH HHS/ -- R01 HG004037/HG/NHGRI NIH HHS/ -- RC2HG005679/HG/NHGRI NIH HHS/ -- U01 HG004267/HG/NHGRI NIH HHS/ -- U01HG004264/HG/NHGRI NIH HHS/ -- U01HG004267/HG/NHGRI NIH HHS/ -- U54 HG004558/HG/NHGRI NIH HHS/ -- U54 HG006996/HG/NHGRI NIH HHS/ -- U54HG004558/HG/NHGRI NIH HHS/ -- U54HG006996/HG/NHGRI NIH HHS/ -- UL1 TR000430/TR/NCATS NIH HHS/ -- England -- Nature. 2014 Aug 28;512(7515):453-6. doi: 10.1038/nature13668.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉1] Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA [2]. ; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA. ; Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520, USA. ; Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06520, USA. ; Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA. ; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. ; 1] Department of Computer Science, Stanford University, Stanford, California 94305, USA [2] Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. ; 1] Department of Statistics, University of California, Berkeley, California 94720, USA [2] Department of Statistics, University of California, Los Angeles, California 90095, USA. ; Institute for Genomics and Systems Biology, University of Chicago, Chicago, Ilinois 60637, USA. ; National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, 20892, USA. ; Department of Statistics, University of California, Berkeley, California 94720, USA. ; 1] Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA [2] Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25164757" target="_blank"〉PubMed〈/a〉

Description: The discovery of rare genetic variants is accelerating, and clear guidelines for distinguishing disease-causing sequence variants from the many potentially functional variants present in any human genome are urgently needed. Without rigorous standards we risk an acceleration of false-positive reports of causality, which would impede the translation of genomic research findings into the clinical diagnostic setting and hinder biological understanding of disease. Here we discuss the key challenges of assessing sequence variants in human disease, integrating both gene-level and variant-level support for causality. We propose guidelines for summarizing confidence in variant pathogenicity and highlight several areas that require further resource development.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4180223/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4180223/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉MacArthur, D G -- Manolio, T A -- Dimmock, D P -- Rehm, H L -- Shendure, J -- Abecasis, G R -- Adams, D R -- Altman, R B -- Antonarakis, S E -- Ashley, E A -- Barrett, J C -- Biesecker, L G -- Conrad, D F -- Cooper, G M -- Cox, N J -- Daly, M J -- Gerstein, M B -- Goldstein, D B -- Hirschhorn, J N -- Leal, S M -- Pennacchio, L A -- Stamatoyannopoulos, J A -- Sunyaev, S R -- Valle, D -- Voight, B F -- Winckler, W -- Gunter, C -- P30 DK020595/DK/NIDDK NIH HHS/ -- P30 DK042086/DK/NIDDK NIH HHS/ -- R01 HG007022/HG/NHGRI NIH HHS/ -- R01 HL117626/HL/NHLBI NIH HHS/ -- R01 MH101810/MH/NIMH NIH HHS/ -- U54 HG006997/HG/NHGRI NIH HHS/ -- England -- Nature. 2014 Apr 24;508(7497):469-76. doi: 10.1038/nature13127.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉1] Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA [2] Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA. ; Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, Maryland 20892, USA. ; Division of Genetics, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, USA. ; 1] Laboratory for Molecular Medicine, Partners Healthcare Center for Personalized Genetic Medicine, Cambridge, Massachusetts 02139, USA [2] Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, USA. ; Department of Genome Sciences, University of Washington, Seattle, Washington 98115, USA. ; Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA. ; 1] NIH Undiagnosed Diseases Program, National Institutes of Health Office of Rare Diseases Research and National Human Genome Research Institute, Bethesda, Maryland 20892, USA [2] Office of the Clinical Director, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA. ; Departments of Bioengineering & Genetics, Stanford University, Stanford, California 94305, USA. ; 1] Department of Genetic Medicine, University of Geneva Medical School, 1211 Geneva, Switzerland [2] iGE3 Institute of Genetics and Genomics of Geneva, 1211 Geneva, Switzerland. ; Center for Inherited Cardiovascular Disease, Stanford University School of Medicine, Stanford, California 94305, USA. ; Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1HH, UK. ; Genetic Disease Research Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland 20892, USA. ; Departments of Genetics, Pathology and Immunology, Washington University School of Medicine, St Louis, Missouri 63110, USA. ; HudsonAlpha Institute for Biotechnology, 601 Genome Way, Huntsville, Alabama 35806, USA. ; Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA. ; 1] Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520, USA [2] Departments of Computer Science, Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut 06520, USA. ; Center for Human Genome Variation, Duke University School of Medicine, Durham, North Carolina 27708, USA. ; 1] Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA [2] Divisions of Genetics and Endocrinology, Children's Hospital, Boston, Massachusetts 02115, USA. ; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA. ; 1] Genomics Division, MS 84-171, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA [2] US Department of Energy Joint Genome Institute, Walnut Creek, California 94598, USA. ; Department of Genome Sciences, University of Washington, 1705 Northeast Pacific Street, Seattle, Washington 98195, USA. ; 1] Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA [2] Harvard Medical School, Boston, Massachusetts 02115, USA. ; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA. ; Department of Pharmacology and Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA. ; 1] Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA [2] Next Generation Diagnostics, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, USA (W.W.); Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, Georgia 30329, USA (C.G.). ; 1] HudsonAlpha Institute for Biotechnology, 601 Genome Way, Huntsville, Alabama 35806, USA [2] Next Generation Diagnostics, Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, USA (W.W.); Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, Georgia 30329, USA (C.G.).〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/24759409" target="_blank"〉PubMed〈/a〉

Description: Activation of executioner caspases is a predictor of progression-free survival in glioblastoma patients: a systems medicine approach Cell Death and Disease 4, e629 (May 2013). doi:10.1038/cddis.2013.157 Authors: Á C Murphy, B Weyhenmeyer, J Schmid, S M Kilbride, M Rehm, H J Huber, C Senft, J Weissenberger, V Seifert, M Dunst, M Mittelbronn, D Kögel, J H M Prehn & B M Murphy

Description: From computational modelling of the intrinsic apoptosis pathway to a systems-based analysis of chemotherapy resistance: achievements, perspectives and challenges in systems medicine Cell Death and Disease 5, e1258 (May 2014). doi:10.1038/cddis.2014.36 Authors: M L Würstle, E Zink, J H M Prehn & M Rehm