Ephrin-B2 expression critically influences Nipah virus infection independent of its cytoplasmic tail

<p>Abstract</p> <p>Background</p> <p>Cell entry and cell-to-cell spread of the highly pathogenic Nipah virus (NiV) requires binding of the NiV G protein to cellular ephrin receptors and subsequent NiV F-mediated fusion. Since expression levels of the main NiV entry receptor ephrin-B2 (EB2) are highly regulated <it>in vivo </it>to fulfill the physiological functions in axon guidance and angiogenesis, the goal of this study was to determine if changes in the EB2 expression influence NiV infection.</p> <p>Results</p> <p>Surprisingly, transfection of increasing EB2 plasmid concentrations reduced cell-to-cell fusion both in cells expressing the NiV glycoproteins and in cells infected with NiV. This effect was attributed to the downregulation of the NiV glycoproteins from the cell surface. In addition to the influence on cell-to-cell fusion, increased EB2 expression significantly reduced the total amount of NiV-infected cells, thus interfered with virus entry. To determine if the negative effect of elevated EB2 expression on virus entry is a result of an increased EB2 signaling, receptor function of a tail-truncated and therefore signaling-defective ΔcEB2 was tested. Interestingly, ΔcEB2 fully functioned as NiV entry and fusion receptor, and overexpression also interfered with virus replication.</p> <p>Conclusion</p> <p>Our findings clearly show that EB2 signaling does not account for the striking negative impact of elevated receptor expression on NiV infection, but rather that the ratio between the NiV envelope glycoproteins and surface receptors critically influence cell-to-cell fusion and virus entry.</p

T-cadherin attenuates insulin-dependent signalling, eNOS activation, and angiogenesis in vascular endothelial cells

Aims T-cadherin (T-cad) is a glycosylphosphatidylinositol-anchored cadherin family member. Experimental, clinical, and genomic studies suggest a role for T-cad in vascular disorders such as atherosclerosis and hypertension, which are associated with endothelial dysfunction and insulin resistance (InsRes). In endothelial cells (EC), T-cad and insulin activate similar signalling pathways [e.g. PI3-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR)] and processes (e.g. angiogenesis). We hypothesize that T-cad is a regulatory component of insulin signalling in EC and therefore a determinant of the development of endothelial InsRes. Methods and results We investigated T-cad-dependent effects on insulin sensitivity using human EC stably transduced with respect to T-cad overexpression or T-cad silencing. Responsiveness to insulin was examined at the level of effectors of the insulin signalling cascade, EC nitric oxide synthase (eNOS) activation, and angiogenic behaviour. Overexpression and ligation of T-cad on EC attenuates insulin-dependent activation of the PI3K/Akt/mTOR signalling axis, eNOS, EC migration, and angiogenesis. Conversely, T-cad silencing enhances these actions of insulin. Attenuation of EC responsiveness to insulin results from T-cad-mediated chronic activation of the Akt/mTOR-dependent negative feedback loop of the insulin cascade and enhanced degradation of the insulin receptor (IR) substrate. Co-immunoprecipitation experiments revealed an association between T-cad and IR. Filipin abrogated inhibitory effects of T-cad on insulin signalling, demonstrating localization of T-cad-insulin cross-talk to lipid raft plasma membrane domains. Hyperinsulinaemia up-regulates T-cad mRNA and protein levels in EC. Conclusion T-cad expression modulates signalling and functional responses of EC to insulin. We have identified a novel signalling mechanism regulating insulin function in the endothelium and attribute a role for T-cad up-regulation in the pathogenesis of endothelial InsRe

Continuous presence of genetically diverse rustrela virus lineages in yellow-necked field mouse reservoir populations in northeastern Germany.

Rustrela virus (RusV; species Rubivirus strelense, family Matonaviridae) was discovered in different zoo animal species affected by fatal encephalitis. Simultaneous RusV RNA detection in multiple yellow-necked field mice (Apodemus flavicollis) suggested this rodent as a reservoir of RusV. Here, we investigated 1,264 yellow-necked field mice and sympatric other small mammals from different regions in Germany for RusV RNA using an optimized reverse transcription-quantitative polymerase chain reaction (RT-qPCR) protocol and high-throughput sequencing. The investigation resulted in the detection of RusV RNA exclusively in 50 of 396 (12.6 per cent) yellow-necked field mice but absence in other sympatric species. RT-qPCR-determined tissue distribution of RusV RNA revealed the highest viral loads in the central nervous system, with other tissues being only very rarely affected. The histopathological evaluation did not reveal any hints of encephalitis in the brains of infected animals despite the detection of viral RNA in neurons by in situ hybridization (ISH). The positive association between the body mass of yellow-necked field mice and RusV RNA detection suggests a persistent infection. Phylogenetic analysis of partial E1 and full-genome sequences showed a high diversification with at least four RusV lineages (1A-1D) in northeastern Germany. Moreover, phylogenetic and isolation-by-distance analyses indicated evolutionary processes of RusV mostly in local reservoir populations. A comparison of complete genome sequences from all detected RusV lineages demonstrated a high level of amino acid and nucleotide sequence variability within a part of the p150 peptide of the non-structural polyprotein and its coding sequence, respectively. The location of this region within the RusV genome and its genetic properties were comparable to the hypervariable region of the rubella virus. The broad range of detected RusV spillover hosts in combination with its geographical distribution in northeastern Germany requires the assessment of its zoonotic potential and further analysis of encephalitis cases in mammals. Future studies have to prove a putative co-evolution scenario for RusV in the yellow-necked field mouse reservoir

Mystery of fatal 'staggering disease' unravelled: novel rustrela virus causes severe meningoencephalomyelitis in domestic cats

‘Staggering disease’ is a neurological disease entity considered a threat to European domestic cats (Felis catus) for almost five decades. However, its aetiology has remained obscure. Rustrela virus (RusV), a relative of rubella virus, has recently been shown to be associated with encephalitis in a broad range of mammalian hosts. Here, we report the detection of RusV RNA and antigen by metagenomic sequencing, RT-qPCR, in-situ hybridization and immunohistochemistry in brain tissues of 27 out of 29 cats with non-suppurative meningoencephalomyelitis and clinical signs compatible with’staggering disease’ from Sweden, Austria, and Germany, but not in non-affected control cats. Screening of possible reservoir hosts in Sweden revealed RusV infection in wood mice (Apodemus sylvaticus). Our work indicates that RusV is the long-sought cause of feline ‘staggering disease’. Given its reported broad host spectrum and considerable geographic range, RusV may be the aetiological agent of neuropathologies in further mammals, possibly even including humans

Genetic Admixture and Population Substructure in Guanacaste Costa Rica

The population of Costa Rica (CR) represents an admixture of major continental populations. An investigation of the CR population structure would provide an important foundation for mapping genetic variants underlying common diseases and traits. We conducted an analysis of 1,301 women from the Guanacaste region of CR using 27,904 single nucleotide polymorphisms (SNPs) genotyped on a custom Illumina InfiniumII iSelect chip. The program STRUCTURE was used to compare the CR Guanacaste sample with four continental reference samples, including HapMap Europeans (CEU), East Asians (JPT+CHB), West African Yoruba (YRI), as well as Native Americans (NA) from the Illumina iControl database. Our results show that the CR Guanacaste sample comprises a three-way admixture estimated to be 43% European, 38% Native American and 15% West African. An estimated 4% residual Asian ancestry may be within the error range. Results from principal components analysis reveal a correlation between genetic and geographic distance. The magnitude of linkage disequilibrium (LD) measured by the number of tagging SNPs required to cover the same region in the genome in the CR Guanacaste sample appeared to be weaker than that observed in CEU, JPT+CHB and NA reference samples but stronger than that of the HapMap YRI sample. Based on the clustering pattern observed in both STRUCTURE and principal components analysis, two subpopulations were identified that differ by approximately 20% in LD block size averaged over all LD blocks identified by Haploview. We also show in a simulated association study conducted within the two subpopulations, that the failure to account for population stratification (PS) could lead to a noticeable inflation in the false positive rate. However, we further demonstrate that existing PS adjustment approaches can reduce the inflation to an acceptable level for gene discovery

Gene expression patterns in four brain areas associate with quantitative measure of estrous behavior in dairy cows

<p>Abstract</p> <p>Background</p> <p>The decline noticed in several fertility traits of dairy cattle over the past few decades is of major concern. Understanding of the genomic factors underlying fertility, which could have potential applications to improve fertility, is very limited. Here, we aimed to identify and study those genes that associated with a key fertility trait namely estrous behavior, among genes expressed in four bovine brain areas (hippocampus, amygdala, dorsal hypothalamus and ventral hypothalamus), either at the start of estrous cycle, or at mid cycle, or regardless of the phase of cycle.</p> <p>Results</p> <p>An average heat score was calculated for each of 28 primiparous cows in which estrous behavior was recorded for at least two consecutive estrous cycles starting from 30 days post-partum. Gene expression was then measured in brain tissue samples collected from these cows, 14 of which were sacrificed at the start of estrus and 14 around mid cycle. For each brain area, gene expression was modeled as a function of the orthogonally transformed average heat score values using a Bayesian hierarchical mixed model. Genes whose expression patterns showed significant linear or quadratic relationships with heat scores were identified. These included genes expected to be related to estrous behavior as they influence states like socio-sexual behavior, anxiety, stress and feeding motivation (<it>OXT, AVP, POMC, MCHR1</it>), but also genes whose association with estrous behavior is novel and warrants further investigation.</p> <p>Conclusions</p> <p>Several genes were identified whose expression levels in the bovine brain associated with the level of expression of estrous behavior. The genes <it>OXT </it>and <it>AVP </it>play major roles in regulating estrous behavior in dairy cows. Genes related to neurotransmission and neuronal plasticity are also involved in estrous regulation, with several genes and processes expressed in mid-cycle probably contributing to proper expression of estrous behavior in the next estrus. Studying these genes and the processes they control improves our understanding of the genomic regulation of estrous behavior expression.</p

Crop Updates 2006 - Lupins and Pulses

This session covers sixty six papers from different authors: 2005 LUPIN AND PULSE INDUSTRY HIGHLIGHTS 1. Lupin Peter White, Department of Agriculture 2. Pulses Mark Seymour, Department of Agriculture 3. Monthly rainfall at experimental sites in 2005 4. Acknowledgements Amelia McLarty EDITOR 5. Contributors 6. Background Peter White, Department of Agriculture 2005 REGIONAL ROUNDUP 7. Northern agricultural region Wayne Parker, Department of Agriculture 8. Central agricultural region Ian Pritchard and Bob French, Department of Agriculture 9. Great southern and lakes Rodger Beermier, Department of Agriculture 10. South east region Mark Seymour, Department of Agriculture LUPIN AND PULSE PRODUCTION AGRONOMY AND GENETIC IMPROVEMENT 11. Lupin Peter White, Department of Agriculture 12. Narrow-leafed lupin breeding Bevan Buirchell, Department of Agriculture 13. Progress in the development of pearl lupin (Lupinus mutabilis) for Australian agriculture, Mark Sweetingham1,2, Jon Clements1, Geoff Thomas2, Roger Jones1, Sofia Sipsas1, John Quealy2, Leigh Smith1 and Gordon Francis1 1CLIMA, The University of Western Australia 2Department of Agriculture 14. Molecular genetic markers and lupin breeding, Huaan Yang, Jeffrey Boersma, Bevan Buirchell, Department of Agriculture 15. Construction of a genetic linkage map using MFLP, and identification of molecular markers linked to domestication genes in narrow-leafed lupin (Lupinus augustiflolius L) Jeffrey Boersma1,2, Margaret Pallotta3, Bevan Buirchell1, Chengdao Li1, Krishnapillai Sivasithamparam2 and Huaan Yang1 1Department of Agriculture, 2The University of Western Australia, 3Australian Centre for Plant Functional Genomics, South Australia 16. The first gene-based map of narrow-leafed lupin – location of domestication genes and conserved synteny with Medicago truncatula, M. Nelson1, H. Phan2, S. Ellwood2, P. Moolhuijzen3, M. Bellgard3, J. Hane2, A. Williams2, J. Fos‑Nyarko4, B. Wolko5, M. Książkiewicz5, M. Cakir4, M. Jones4, M. Scobie4, C. O’Lone1, S.J. Barker1, R. Oliver2, and W. Cowling1 1School of Plant Biology, The University of Western Australia, 2Australian Centre for Necrotrophic Fungal Pathogens, Murdoch University, 3Centre for Bioinformatics and Biological Computing, Murdoch University, 4School of Biological Sciences and Biotechnology, SABC, Murdoch University,5Institute of Plant Genetics, Polish Academy of Sciences, Poznań, Poland 17. How does lupin optimum density change row spacing? Bob French and Laurie Maiolo, Department of Agriculture 18. Wide row spacing and seeding rate of lupins with conventional and precision seeding machines Martin Harries, Jo Walker and Murray Blyth, Department of Agriculture 19. Influence of row spacing and plant density on lupin competition with annual ryegrass, Martin Harries, Jo Walker and Murray Blyth, Department of Agriculture 20. Effect of timing and speed of inter-row cultivation on lupins, Martin Harries, Jo Walker and Steve Cosh, Department of Agriculture 21. The interaction of atrazine herbicide rate and row spacing on lupin seedling survival, Martin Harries and Jo Walker Department of Agriculture 22. The banding of herbicides on lupin row crops, Martin Harries, Jo Walker and Murray Blyth, Department of Agriculture 23. Large plot testing of herbicide tolerance of new lupin lines, Wayne Parker, Department of Agriculture 24. Effect of seed source and simazine rate of seedling emergence and growth, Peter White and Greg Shea, Department of Agriculture 25. The effect of lupin row spacing and seeding rate on a following wheat crop, Martin Harries, Jo Walker and Dirranie Kirby, Department of Agriculture 26. Response of crop lupin species to row spacing, Leigh Smith1, Kedar Adhikari1, Jon Clements2 and Patrizia Guantini3, 1Department of Agriculture, 2CLIMA, The University of Western Australia, 3University of Florence, Italy 27. Response of Lupinus mutabilis to lime application and over watering, Peter White, Leigh Smith and Mark Sweetingham, Department of Agriculture 28. Impact of anthracnose on yield of Andromeda lupins, Geoff Thomas, Kedar Adhikari and Katie Bell, Department of Agriculture 29. Survey of lupin root health (in major production areas), Geoff Thomas, Ken Adcock, Katie Bell, Ciara Beard and Anne Smith, Department of Agriculture 30. Development of a generic forecasting and decision support system for diseases in the Western Australian wheatbelt, Tim Maling1, Art Diggle1,2, Debbie Thackray1, Kadambot Siddique1 and Roger Jones1,2 1CLIMA, The University of Western Australia, 2Department of Agriculture 31.Tanjil mutants highly tolerant to metribuzin, Ping Si1, Mark Sweetingham1,2, Bevan Buirchell1,2 and Huaan Yang l,2 1CLIMA, The University of Western Australia, 2Department of Agriculture 32. Precipitation pH vs. yield and functional properties of lupin protein isolate, Vijay Jayasena1, Hui Jun Chih1 and Ken Dods2 1Curtin University of Technology, 2Chemistry Centre 33. Lupin protein isolation with the use of salts, Vijay Jayasena1, Florence Kartawinata1,Ranil Coorey1 and Ken Dods2 1Curtin University of Technology, 2Chemistry Centre 34. Field pea, Mark Seymour, Department of Agriculture 35. Breeding highlights Kerry Regan1,2, Tanveer Khan1,2, Stuart Morgan1 and Phillip Chambers1 1Department of Agriculture, 2CLIMA, The University of Western Australia 36. Variety evaluation, Kerry Regan1,2, Tanveer Khan1,2, Jenny Garlinge1 and Rod Hunter1 1Department of Agriculture, 2CLIMA, The University of Western Australia 37. Days to flowering of field pea varieties throughout WA Mark Seymour1, Ian Pritchard1, Rodger Beermier1, Pam Burgess1 and Dr Eric Armstrong2 Department of Agriculture, 2NSW Department of Primary Industries, Wagga Wagga 38. Semi-leafless field peas yield more, with less ryegrass seed set, in narrow rows, Glen Riethmuller, Department of Agriculture 39. Swathing, stripping and other innovative ways to harvest field peas, Mark Seymour, Ian Pritchard, Rodger Beermier and Pam Burgess, Department of Agriculture 40. Pulse demonstrations, Ian Pritchard, Wayne Parker, Greg Shea, Department of Agriculture 41. Field pea extension – focus on field peas 2005, Ian Pritchard, Department of Agriculture 42. Field pea blackspot disease in 2005: Prediction versus reality, Moin Salam, Jean Galloway, Pip Payne, Bill MacLeod and Art Diggle, Department of Agriculture 43. Pea seed-borne mosaic virus in pulses: Screening for seed quality defects and virus resistance, Rohan Prince, Brenda Coutts and Roger Jones, Department of Agriculture, and CLIMA, The University of Western Australia 44. Yield losses from sowing field peas infected with pea seed-borne mosaic virus, Rohan Prince, Brenda Coutts and Roger Jones, Department of Agriculture, and CLIMA, The University of Western Australia 45. Desi chickpea, Wayne Parker, Department of Agriculture 46. Breeding highlights, Tanveer Khan 1,2, Pooran Gaur3, Kadambot Siddique2, Heather Clarke2, Stuart Morgan1and Alan Harris1, 1Department of Agriculture2CLIMA, The University of Western Australia, 3International Crop Research Institute for Semi Arid Tropics (ICRISAT), India 47. National chickpea improvement program, Kerry Regan1, Ted Knights2 and Kristy Hobson3,1Department of Agriculture, 2Agriculture New South Wales 3Department of Primary Industries, Victoria 48. Chickpea breeding lines in CVT exhibit excellent ascochyta blight resistance, Tanveer Khan1,2, Alan Harris1, Stuart Morgan1 and Kerry Regan1,2, 1Department of Agriculture, 2CLIMA, The University of Western Australia 49. Variety evaluation, Kerry Regan1,2, Tanveer Khan1,2, Jenny Garlinge2 and Rod Hunter2, 1CLIMA, The University of Western Australia 2Department of Agriculture 50. Desi chickpeas for the wheatbelt, Wayne Parker and Ian Pritchard, Department of Agriculture 51. Large scale demonstration of new chickpea varieties, Wayne Parker, MurrayBlyth, Steve Cosh, Dirranie Kirby and Chris Matthews, Department of Agriculture 52. Ascochyta management with new chickpeas, Martin Harries, Bill MacLeod, Murray Blyth and Jo Walker, Department of Agriculture 53. Management of ascochyta blight in improved chickpea varieties, Bill MacLeod1, Colin Hanbury2, Pip Payne1, Martin Harries1, Murray Blyth1, Tanveer Khan1,2, Kadambot Siddique2, 1Department of Agriculture, 2CLIMA, The University of Western Australia 54. Botrytis grey mould of chickpea, Bill MacLeod, Department of Agriculture 55. Kabuli chickpea, Kerry Regan, Department of Agriculture, and CLIMA, The University of Western Australia 56. New ascochyta blight resistant, high quality kabuli chickpea varieties, Kerry Regan1,2, Kadambot Siddique2, Tim Pope2 and Mike Baker1, 1Department of Agriculture, 2CLIMA, The University of Western Australia 57. Crop production and disease management of Almaz and Nafice, Kerry Regan and Bill MacLeod, Department of Agriculture, and CLIMA, The University of Western Australia 58. Faba bean,Mark Seymour, Department of Agriculture 59. Germplasm evaluation – faba bean, Mark Seymour1, Tim Pope2, Peter White1, Martin Harries1, Murray Blyth1, Rodger Beermier1, Pam Burgess1 and Leanne Young1,1Department of Agriculture, 2CLIMA, The University of Western Australia 60. Factors affecting seed coat colour of faba bean during storage, Syed Muhammad Nasar-Abbas1, Julie Plummer1, Kadambot Siddique2, Peter White 3, D. Harris4 and Ken Dods4.1The University of Western Australia, 2CLIMA, The University of Western Australia, 3Department of Agriculture, 4Chemistry Centre 61. Lentil,Kerry Regan, Department of Agriculture, and CLIMA, The University of Western Australia 62. Variety and germplasm evaluation, Kerry Regan1,2, Tim Pope2, Leanne Young1, Phill Chambers1, Alan Harris1, Wayne Parker1 and Michael Materne3, 1Department of Agriculture 2CLIMA, The University of Western Australia, 3Department of Primary Industries, Victoria Pulse species 63. Land suitability for production of different crop species in Western Australia, Peter White, Dennis van Gool, and Mike Baker, Department of Agriculture 64. Genomic synteny in legumes: Application to crop breeding, Huyen Phan1, Simon Ellwood1, J. Hane1, Angela Williams1, R. Ford2, S. Thomas3 and Richard Oliver1,1Australian Centre of Necrotrophic Plant Pathogens, Murdoch University 2BioMarka, School of Agriculture and Food Systems, ILFR, University of Melbourne 3NSW Department of Primary Industries 65. ALOSCA – Development of a dry flow legume seed inoculant, Rory Coffey and Chris Poole, ALOSCA Technologies Pty Ltd 66. Genetic dissection of resistance to fungal necrotrophs in Medicago truncatula, Simon Ellwood1, Theo Pfaff1, Judith Lichtenzveig12, Lars Kamphuis1, Nola D\u27Souza1, Angela Williams1, Emma Groves1, Karam Singh2 and Richard Oliver1 1Australian Centre of Necrotrophic Plant Pathogens, Murdoch University, 2CSIRO Plant Industry APPENDIX I: LIST OF COMMON ACRONYM

Community forest management in Indonesia: Avoided deforestation in the context of anthropogenic and climate complexities

Community forest management has been identified as a win-win option for reducing deforestation while improving the welfare of rural communities in developing countries. Despite considerable investment in community forestry globally, systematic evaluations of the impact of these policies at appropriate scales are lacking. We assessed the extent to which deforestation has been avoided as a result of the Indonesian government’s community forestry scheme, Hutan Desa (Village Forest). We used annual data on deforestation rates between 2012 and 2016 from two rapidly developing islands: Sumatra and Kalimantan. The total area of Hutan Desa increased from 750 km2 in 2012 to 2500 km2 in 2016. We applied a spatial matching approach to account for biophysical variables affecting deforestation and Hutan Desa selection criteria. Performance was assessed relative to a counterfactual likelihood of deforestation in the absence of Hutan Desa tenure. We found that Hutan Desa management has successfully achieved avoided deforestation overall, but performance has been increasingly variable through time. Hutan Desa performance was influenced by anthropogenic and climatic factors, as well as land use history. Hutan Desa allocated on watershed protection forest or limited production forest typically led to a less avoided deforestation regardless of location. Conversely, Hutan Desa granted on permanent or convertible production forest had variable performance across different years and locations. The amount of rainfall during the dry season in any given year was an important climatic factor influencing performance. Extremely dry conditions during drought years pose additional challenges to Hutan Desa management, particularly on peatland, due to increased vulnerability to fire outbreaks. This study demonstrates how the performance of Hutan Desa in avoiding deforestation is fundamentally affected by biophysical and anthropogenic circumstances over time and space. Our study improves understanding on where and when the policy is most effective with respect to deforestation, and helps identify opportunities to improve policy implementation. This provides an important first step towards evaluating the overall effectiveness of this policy in achieving both social and environmental goals