CITES, wild plants, and opportunities for crime

The illegal trade in endangered plants damages both the environment and local communities by threatening and destroying numerous species and important natural resources. There is very little research which systematically addresses this issue by identifying specific opportunities for crime. This article presents the results of an interdisciplinary study which brings together criminological and conservation science expertise to identify criminal opportunities in the illegal wild plant trade and suggest strategies in order to prevent and mitigate the problem. Methodologically, the study adapts a crime proofing of legislation approach to the UN Convention on the International Trade in Endangered Species of Wild Fauna and Flora and is based on documentary and interview data. Situational crime prevention is used as a framework to provide points for effective intervention

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Gene targeting in adult rhesus macaque fibroblasts

<p>Abstract</p> <p>Background</p> <p>Gene targeting in nonhuman primates has the potential to produce critical animal models for translational studies related to human diseases. Successful gene targeting in fibroblasts followed by somatic cell nuclear transfer (SCNT) has been achieved in several species of large mammals but not yet in primates. Our goal was to establish the protocols necessary to achieve gene targeting in primary culture of adult rhesus macaque fibroblasts as a first step in creating nonhuman primate models of genetic disease using nuclear transfer technology.</p> <p>Results</p> <p>A primary culture of adult male fibroblasts was transfected with hTERT to overcome senescence and allow long term <it>in vitro </it>manipulations. Successful gene targeting of the HPRT locus in rhesus macaques was achieved by electroporating S-phase synchronized cells with a construct containing a SV40 enhancer.</p> <p>Conclusion</p> <p>The cell lines reported here could be used for the production of null mutant rhesus macaque models of human genetic disease using SCNT technology. In addition, given the close evolutionary relationship and biological similarity between rhesus macaques and humans, the protocols described here may prove useful in the genetic engineering of human somatic cells.</p

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Topology analysis and visualization of Potyvirus protein-protein interaction network

Background: One of the central interests of Virology is the identification of host factors that contribute to virus infection. Despite tremendous efforts, the list of factors identified remains limited. With omics techniques, the focus has changed from identifying and thoroughly characterizing individual host factors to the simultaneous analysis of thousands of interactions, framing them on the context of protein-protein interaction networks and of transcriptional regulatory networks. This new perspective is allowing the identification of direct and indirect viral targets. Such information is available for several members of the Potyviridae family, one of the largest and more important families of plant viruses. Results: After collecting information on virus protein-protein interactions from different potyviruses, we have processed it and used it for inferring a protein-protein interaction network. All proteins are connected into a single network component. Some proteins show a high degree and are highly connected while others are much less connected, with the network showing a significant degree of dissortativeness. We have attempted to integrate this virus protein-protein interaction network into the largest protein-protein interaction network of Arabidopsis thaliana, a susceptible laboratory host. To make the interpretation of data and results easier, we have developed a new approach for visualizing and analyzing the dynamic spread on the host network of the local perturbations induced by viral proteins. We found that local perturbations can reach the entire host protein-protein interaction network, although the efficiency of this spread depends on the particular viral proteins. By comparing the spread dynamics among viral proteins, we found that some proteins spread their effects fast and efficiently by attacking hubs in the host network while other proteins exert more local effects. Conclusions: Our findings confirm that potyvirus protein-protein interaction networks are highly connected, with some proteins playing the role of hubs. Several topological parameters depend linearly on the protein degree. Some viral proteins focus their effect in only host hubs while others diversify its effect among several proteins at the first step. Future new data will help to refine our model and to improve our predictions.This work was supported by the Spanish Ministerio de Economia y Competitividad grants BFU2012-30805 (to SFE), DPI2011-28112-C04-02 (to AF) and DPI2011-28112-C04-01 (to JP). The first two authors are recipients of fellowships from the Spanish Ministerio de Economia y Competitividad: BES-2012-053772 (to GB) and BES-2012-057812 (to AF-F).Bosque, G.; Folch Fortuny, A.; Picó Marco, JA.; Ferrer, A.; Elena Fito, SF. (2014). Topology analysis and visualization of Potyvirus protein-protein interaction network. BMC Systems Biology. 129(8):1-15. doi:10.1186/s12918-014-0129-8S1151298Gibbs A, Ohshima K: Potyviruses and the digital revolution. Annu Rev Phytopathol. 2010, 48: 205-223. 10.1146/annurev-phyto-073009-114404.Spence NJ, Phiri NA, Hughes SL, Mwaniki A, Simons S, Oduor G, Chacha D, Kuria A, Ndirangu S, Kibata GN, Marris GC: Economic impact of turnip mosaic virus, cauliflower mosaic virus and beet mosaic virus in three Kenyan vegetables. Plant Pathol. 2007, 56: 317-323. 10.1111/j.1365-3059.2006.01498.x.Ward CW, Shukla DD: Taxonomy of potyviruses: current problems and some solutions. Intervirology. 1991, 32: 269-296.Riechmann JL, Laín S, García JA: Highlights and prospects of potyvirus molecular biology. J Gen Virol. 1992, 73 (Pt 1): 1-16. 10.1099/0022-1317-73-1-1.Elena SF, Rodrigo G: Towards an integrated molecular model of plant-virus interactions. Curr Opin Virol. 2012, 2: 719-724. 10.1016/j.coviro.2012.09.004.Wei T, Zhang C, Hong J, Xiong R, Kasschau KD, Zhou X, Carrington JC, Wang A: Formation of complexes at plasmodesmata for potyvirus intercellular movement is mediated by the viral protein P3N-PIPO. PLoS Pathog. 2010, 6: e1000962-10.1371/journal.ppat.1000962.Chung BY-W, Miller WA, Atkins JF, Firth AE: An overlapping essential gene in the Potyviridae. Proc Natl Acad Sci. 2008, 105: 5897-5902. 10.1073/pnas.0800468105.Allison R, Johnston RE, Dougherty WG: The nucleotide sequence of the coding region of tobacco etch virus genomic RNA: evidence for the synthesis of a single polyprotein. Virology. 1986, 154: 9-20. 10.1016/0042-6822(86)90425-3.Domier LL, Franklin KM, Shahabuddin M, Hellmann GM, Overmeyer JH, Hiremath ST, Siaw MF, Lomonossoff GP, Shaw JG, Rhoads RE: The nucleotide sequence of tobacco vein mottling virus RNA. Nucleic Acids Res. 1986, 14: 5417-5430. 10.1093/nar/14.13.5417.Revers F, Le Gall O, Candresse T, Maule AJ: New advances in understanding the molecular biology of plant/potyvirus interactions. Mol Plant Microbe Interact. 1999, 12: 367-376. 10.1094/MPMI.1999.12.5.367.Urcuqui-Inchima S, Haenni AL, Bernardi F: Potyvirus proteins: a wealth of functions. Virus Res. 2001, 74: 157-175. 10.1016/S0168-1702(01)00220-9.Merits A, Rajamäki M-L, Lindholm P, Runeberg-Roos P, Kekarainen T, Puustinen P, Mäkeläinen K, Valkonen JPT, Saarma M: Proteolytic processing of potyviral proteins and polyprotein processing intermediates in insect and plant cells. J Gen Virol. 2002, 83: 1211-1221.Adams MJ, Antoniw JF, Beaudoin F: Overview and analysis of the polyprotein cleavage sites in the family Potyviridae. Mol Plant Pathol. 2005, 6: 471-487. 10.1111/j.1364-3703.2005.00296.x.Zheng H, Yan F, Lu Y, Sun L, Lin L, Cai L, Hou M, Chen J: Mapping the self-interacting domains of TuMV HC-Pro and the subcellular localization of the protein. Virus Genes. 2011, 42: 110-116. 10.1007/s11262-010-0538-8.Culver JN, Padmanabhan MS: Virus-induced disease: altering host physiology one interaction at a time. Annu Rev Phytopathol. 2007, 45: 221-243. 10.1146/annurev.phyto.45.062806.094422.De Las Rivas J, Fontanillo C: Protein-protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Comput Biol. 2010, 6: e1000807-10.1371/journal.pcbi.1000807.Bornke F: Protein Interaction Networks. Anal Biol Netw. Edited by: Junker BH, Schreiber F. 2008, John Wiley & Sons, Inc, Hoboken, NJ, USA, 207-232. 10.1002/9780470253489.ch9.Phizicky EM, Fields S: Protein-protein interactions: methods for detection and analysis. Microbiol Rev. 1995, 59: 94-123.Brückner A, Polge C, Lentze N, Auerbach D, Schlattner U: Yeast two-hybrid, a powerful tool for systems biology. Int J Mol Sci. 2009, 10: 2763-2788. 10.3390/ijms10062763.Fields S, Song O: A novel genetic system to detect protein-protein interactions. Nature. 1989, 340: 245-246. 10.1038/340245a0.Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, Adams S-L, Millar A, Taylor P, Bennett K, Boutilier K, Yang L, Wolting C, Donaldson I, Schandorff S, Shewnarane J, Vo M, Taggart J, Goudreault M, Muskat B, Alfarano C, Dewar D, Lin Z, Michalickova K, Willems AR, Sassi H, Nielsen PA, Rasmussen KJ, Andersen JR, Johansen LE, Hansen LH, et al: Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature. 2002, 415: 180-183. 10.1038/415180a.Hu C-D, Chinenov Y, Kerppola TK: Visualization of interactions among bZIP and Rel family proteins in living cells using bimolecular fluorescence complementation. Mol Cell. 2002, 9: 789-798. 10.1016/S1097-2765(02)00496-3.Kodama Y, Hu C-D: An improved bimolecular fluorescence complementation assay with a high signal-to-noise ratio. Biotechniques. 2010, 49: 793-805. 10.2144/000113519.Rual J-F, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, Klitgord N, Simon C, Boxem M, Milstein S, Rosenberg J, Goldberg DS, Zhang LV, Wong SL, Franklin G, Li S, Albala JS, Lim J, Fraughton C, Llamosas E, Cevik S, Bex C, Lamesch P, Sikorski RS, Vandenhaute J, Zoghbi HY, et al: Towards a proteome-scale map of the human protein-protein interaction network. Nature. 2005, 437: 1173-1178. 10.1038/nature04209.Venkatesan K, Rual J-F, Vazquez A, Stelzl U, Lemmens I, Hirozane-Kishikawa T, Hao T, Zenkner M, Xin X, Goh K-I, Yildirim MA, Simonis N, Heinzmann K, Gebreab F, Sahalie JM, Cevik S, Simon C, de Smet A-S, Dann E, Smolyar A, Vinayagam A, Yu H, Szeto D, Borick H, Dricot A, Klitgord N, Murray RR, Lin C, Lalowski M, Timm J, et al: An empirical framework for binary interactome mapping. Nat Methods. 2008, 6: 83-90. 10.1038/nmeth.1280.Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, Lockshon D, Narayan V, Srinivasan M, Pochart P, Qureshi-Emili A, Li Y, Godwin B, Conover D, Kalbfleisch T, Vijayadamodar G, Yang M, Johnston M, Fields S, Rothberg JM: A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature. 2000, 403: 623-627. 10.1038/35001009.Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y: A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci. 2001, 98: 4569-4574. 10.1073/pnas.061034498.Uetz P, Dong Y-A, Zeretzke C, Atzler C, Baiker A, Berger B, Rajagopala SV, Roupelieva M, Rose D, Fossum E, Haas J: Herpesviral protein networks and their interaction with the human proteome. Science. 2006, 311: 239-242. 10.1126/science.1116804.Fossum E, Friedel CC, Rajagopala SV, Titz B, Baiker A, Schmidt T, Kraus T, Stellberger T, Rutenberg C, Suthram S, Bandyopadhyay S, Rose D, von Brunn A, Uhlmann M, Zeretzke C, Dong Y-A, Boulet H, Koegl M, Bailer SM, Koszinowski U, Ideker T, Uetz P, Zimmer R, Haas J: Evolutionarily conserved herpesviral protein interaction networks. PLoS Pathog. 2009, 5: e1000570-10.1371/journal.ppat.1000570.Rodrigo G, Carrera J, Ruiz-Ferrer V, del Toro FJ, Llave C, Voinnet O, Elena SF: A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens. PLoS One. 2012, 7: e40526-10.1371/journal.pone.0040526.Newman MEJ: The structure and function of complex networks. SIAM Rev. 2003, 45: 167-256. 10.1137/S003614450342480.Watts DJ, Strogatz SH: Collective dynamics of "small-world" networks. Nature. 1998, 393: 440-442. 10.1038/30918.Albert R, Barabási A-L: Statistical mechanics of complex networks. Rev Mod Phys. 2002, 74: 47-97. 10.1103/RevModPhys.74.47.Boccaletti S, Latora V, Moreno Y, Chávez M, Hwang D: Complex networks: structure and dynamics. Phys Rep. 2006, 424: 175-308. 10.1016/j.physrep.2005.10.009.Barabási A-L, Oltvai ZN: Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004, 5: 101-113. 10.1038/nrg1272.Albert R, DasGupta B, Hegde R, Sivanathan GS, Gitter A, Gürsoy G, Paul P, Sontag E: Computationally efficient measure of topological redundancy of biological and social networks. Phys Rev E. 2011, 84: 036117-10.1103/PhysRevE.84.036117.Cho D-Y, Kim Y-A, Przytycka TM: Chapter 5: network biology approach to complex diseases. PLoS Comput Biol. 2012, 8: e1002820-10.1371/journal.pcbi.1002820.Russell RB, Aloy P: Targeting and tinkering with interaction networks. Nat Chem Biol. 2008, 4: 666-673. 10.1038/nchembio.119.Winterbach W, Mieghem PV, Reinders M, Wang H, de Ridder D: Topology of molecular interaction networks. BMC Syst Biol. 2013, 7: 90-10.1186/1752-0509-7-90.Pržulj N: Protein-protein interactions: making sense of networks via graph-theoretic modeling. Bioessays. 2011, 33: 115-123. 10.1002/bies.201000044.Yook S-H, Oltvai ZN, Barabási A-L: Functional and topological characterization of protein interaction networks. Proteomics. 2004, 4: 928-942. 10.1002/pmic.200300636.Pržulj N, Wigle DA, Jurisica I: Functional topology in a network of protein interactions. Bioinformatics. 2004, 20: 340-348. 10.1093/bioinformatics/btg415.Elena SF, Carrera J, Rodrigo G: A systems biology approach to the evolution of plant-virus interactions. Curr Opin Plant Biol. 2011, 14: 372-377. 10.1016/j.pbi.2011.03.013.Zilian E, Maiss E: Detection of plum pox potyviral protein-protein interactions in planta using an optimized mRFP-based bimolecular fluorescence complementation system. J Gen Virol. 2011, 92: 2711-2723. 10.1099/vir.0.033811-0.Lin L, Shi Y, Luo Z, Lu Y, Zheng H, Yan F, Chen J, Chen J, Adams MJ, Wu Y: Protein-protein interactions in two potyviruses using the yeast two-hybrid system. Virus Res. 2009, 142: 36-40. 10.1016/j.virusres.2009.01.006.Guo D, Rajamäki M-L, Saarma M, Valkonen JPT: Towards a protein interaction map of potyviruses: protein interaction matrixes of two potyviruses based on the yeast two-hybrid system. J Gen Virol. 2001, 82: 935-939.Shen WT, Wang MQ, Yan P, Gao L, Zhou P: Protein interaction matrix of papaya ringspot virus type P based on a yeast two-hybrid system. Acta Virol. 2010, 54: 49-54. 10.4149/av_2010_01_49.Kang S, Ws L, Kh K: A protein interaction map of soybean mosaic virus strain G7H based on the yeast two-hybrid system. Mol Cells. 2004, 18: 122-126.Yambao MLM, Masuta C, Nakahara K, Uyeda I: The central and C-terminal domains of VPg of Clover yellow vein virus are important for VPg-HCPro and VPg-VPg interactions. J Gen Virol. 2003, 84: 2861-2869. 10.1099/vir.0.19312-0.Evidence for network evolution in an Arabidopsis interactome map. Science. 2011, 333: 601-607. 10.1126/science.1203877.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13: 2498-2504. 10.1101/gr.1239303.Fouss F, Francoisse K, Yen L, Pirotte A, Saerens M: An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification. Neural Netw Off J Int Neural Netw Soc. 2012, 31: 53-72. 10.1016/j.neunet.2012.03.001.Bass JIF, Diallo A, Nelson J, Soto JM, Myers CL, Walhout AJM: Using networks to measure similarity between genes: association index selection. Nat Methods. 2013, 10: 1169-1176. 10.1038/nmeth.2728.Newman MEJ: Assortative mixing in networks. Phys Rev Lett. 2002, 89: 208701-10.1103/PhysRevLett.89.208701

Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study

Background: The risk of severe COVID-19 if an individual becomes infected is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 and how this varies between countries should inform the design of possible strategies to shield or vaccinate those at highest risk. Methods: We estimated the number of individuals at increased risk of severe disease (defined as those with at least one condition listed as “at increased risk of severe COVID-19” in current guidelines) by age (5-year age groups), sex, and country for 188 countries using prevalence data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 and UN population estimates for 2020. The list of underlying conditions relevant to COVID-19 was determined by mapping the conditions listed in GBD 2017 to those listed in guidelines published by WHO and public health agencies in the UK and the USA. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. To help interpretation of the degree of risk among those at increased risk, we also estimated the number of individuals at high risk (defined as those that would require hospital admission if infected) using age-specific infection–hospitalisation ratios for COVID-19 estimated for mainland China and making adjustments to reflect country-specific differences in the prevalence of underlying conditions and frailty. We assumed males were twice at likely as females to be at high risk. We also calculated the number of individuals without an underlying condition that could be considered at increased risk because of their age, using minimum ages from 50 to 70 years. We generated uncertainty intervals (UIs) for our estimates by running low and high scenarios using the lower and upper 95% confidence limits for country population size, disease prevalences, multimorbidity fractions, and infection–hospitalisation ratios, and plausible low and high estimates for the degree of clustering, informed by multimorbidity studies. Findings: We estimated that 1·7 billion (UI 1·0–2·4) people, comprising 22% (UI 15–28) of the global population, have at least one underlying condition that puts them at increased risk of severe COVID-19 if infected (ranging from &lt;5% of those younger than 20 years to &gt;66% of those aged 70 years or older). We estimated that 349 million (186–787) people (4% [3–9] of the global population) are at high risk of severe COVID-19 and would require hospital admission if infected (ranging from &lt;1% of those younger than 20 years to approximately 20% of those aged 70 years or older). We estimated 6% (3–12) of males to be at high risk compared with 3% (2–7) of females. The share of the population at increased risk was highest in countries with older populations, African countries with high HIV/AIDS prevalence, and small island nations with high diabetes prevalence. Estimates of the number of individuals at increased risk were most sensitive to the prevalence of chronic kidney disease, diabetes, cardiovascular disease, and chronic respiratory disease. Interpretation: About one in five individuals worldwide could be at increased risk of severe COVID-19, should they become infected, due to underlying health conditions, but this risk varies considerably by age. Our estimates are uncertain, and focus on underlying conditions rather than other risk factors such as ethnicity, socioeconomic deprivation, and obesity, but provide a starting point for considering the number of individuals that might need to be shielded or vaccinated as the global pandemic unfolds. Funding: UK Department for International Development, Wellcome Trust, Health Data Research UK, Medical Research Council, and National Institute for Health Research

Genome-Wide Analysis of the World's Sheep Breeds Reveals High Levels of Historic Mixture and Strong Recent Selection

Genomic structure in a global collection of domesticated sheep reveals a history of artificial selection for horn loss and traits relating to pigmentation, reproduction, and body size