Surface Co-Expression of Two Different PfEMP1 Antigens on Single Plasmodium falciparum-Infected Erythrocytes Facilitates Binding to ICAM1 and PECAM1

The Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) antigens play a major role in cytoadhesion of infected erythrocytes (IE), antigenic variation, and immunity to malaria. The current consensus on control of variant surface antigen expression is that only one PfEMP1 encoded by one var gene is expressed per cell at a time. We measured var mRNA transcript levels by real-time Q-PCR, analysed var gene transcripts by single-cell FISH and directly compared these with PfEMP1 antigen surface expression and cytoadhesion in three different antibody-selected P. falciparum 3D7 sub-lines using live confocal microscopy, flow cytometry and in vitro adhesion assays. We found that one selected parasite sub-line simultaneously expressed two different var genes as surface antigens, on single IE. Importantly, and of physiological relevance to adhesion and malaria pathogenesis, this parasite sub-line was found to bind both CD31/PECAM1 and CD54/ICAM1 and to adhere twice as efficiently to human endothelial cells, compared to infected cells having only one PfEMP1 variant on the surface. These new results on PfEMP1 antigen expression indicate that a re-evaluation of the molecular mechanisms involved in P. falciparum adhesion and of the accepted paradigm of absolutely mutually exclusive var gene transcription is required

A semi-automated multiplex high-throughput assay for measuring IgG antibodies against <em>Plasmodium falciparum</em> erythrocyte membrane protein 1 (PfEMP1) domains in small volumes of plasma

BACKGROUND: The level of antibodies against PfEMP1 is routinely quantified by the conventional microtitre enzyme-linked immunosorbent assay (ELISA). However, ELISA only measures one analyte at a time and requires a relatively large plasma volume if the complete antibody profile of the sample is to be obtained. Furthermore, assay-to-assay variation and the problem of storage of antigen can influence ELISA results. The bead-based assay described here uses the BioPlex(100 )(BioRad, Hercules, CA, USA) system which can quantify multiple antibodies simultaneously in a small plasma volume. METHODS: A total of twenty nine PfEMP1 domains were PCR amplified from 3D7 genomic DNA, expressed in the Baculovirus system and purified by metal-affinity chromatography. The antibody reactivity level to the recombinant PfEMP1 proteins in human hyper-immune plasma was measured by ELISA. In parallel, these recombinant PfEMP1 proteins were covalently coupled onto beads each having its own unique detection signal and the human hyper-immune plasma reactivity was detected for each individual protein using a BioPlex(100 )system. Protein-coupled beads were analysed at two time points seven months apart, before and after lyophilization and the results compared to determine the effect of storage and lyophilization respectively on the beads. Multiplexed protein-coupled beads from twenty eight unique bead populations were evaluated on the BioPlex(100 )system against pooled human hyper-immune plasma before and after lyophilization. RESULTS: The bead(-)based assay was sensitive, accurate and reproducible. Four recombinant PfEMP1 proteins C17, D5, D9 and D12, selected on the basis that they showed a spread of median fluorescent intensity (MFI) values from low to high when analysed by the bead-based assay were analysed by ELISA and the results from both analyses were highly correlated. The Spearman's rank correlation coefficients (Rho) were ≥ 0.86, (P < 0.0001) for all comparisons. Bead-based assays gave similar results regardless of whether they were performed on individual beads or on multiplexed beads; lyophilization had no impact on the assay performance. Spearman's rank correlation coefficients (Rho) were ≥ 0.97, (P < 0.0001) for all comparisons. Importantly, the reactivity of protein-coupled non-lyophilized beads decreased with long term storage at 4°C in the dark. CONCLUSION: Using this lyophilized multiplex assay, antibody reactivity levels to twenty eight different recombinant PfEMP1 proteins were simultaneously measured using a single microliter of plasma. Thus, the assay reported here provides a useful tool for rapid and efficient quantification of antibody reactivity against PfEMP1 variants in human plasma

Hierarchical, Domain Type-Specific Acquisition of Antibodies to Plasmodium falciparum Erythrocyte Membrane Protein 1 in Tanzanian Children▿ †

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) is a variant antigen expressed on the surface of malaria-infected erythrocytes. PfEMP1 attaches to the vascular lining and allows infected erythrocytes to avoid filtration through the spleen. Each parasite genome encodes about 60 different PfEMP1 variants, each PfEMP1 comprises several domains in its extracellular region, and the PfEMP1 repertoire in different parasites contains domain types that are serologically cross-reactive. In this longitudinal study, we followed 672 children living in an area of high malaria transmission during the first years of life and compared the acquisitions of antibodies to 32 Duffy-binding ligand-like (DBL) domains representing different types. For each child, we determined whether an antibody response to each domain was acquired before, after, or at the same time as responses to each of the other domains. We next used this information to calculate population-level odds ratios to measure the odds that antibodies to a given domain were acquired before antibodies to other domains. Odds ratios for 269 of the 496 possible domain combinations were statistically significant. Thus, the sequence in which individuals acquire antibodies to different PfEMP1 domains is ordered, and children in areas of endemicity first acquire antibodies to particular PfEMP1 domains encoded by the so-called group A and B/A var genes. The results imply that anti-PfEMP1 antibodies effectively structure PfEMP1 expression and play a major role in limiting parasite multiplication in the blood

Development of an updated global land in situ‐based data set of temperature and precipitation extremes: HadEX3

We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorológico Nacional (SMN) of Comisión Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project (https://www.ecad.eu), the SACA&D project (https://saca-bmkg.knmi.nl), and the LACA&D project (https://ciifen.knmi.nl). We thank the three anonymous reviewers for their detailed comments which improved the manuscript.Peer ReviewedPostprint (published version

Development of an Updated Global Land In Situ‐Based Data Set of Temperature and Precipitation Extremes: HadEX3

We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorológico Nacional (SMN) of Comisión Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project (https://www.ecad.eu), the SACA&D project (https://saca-bmkg.knmi.nl), and the LACA&D project (https://ciifen.knmi.nl). We thank the three anonymous reviewers for their detailed comments which improved the manuscript.Peer ReviewedPostprint (published version