Epigallocatechin-3-gallate suppresses the expression of HSP70 and HSP90 and exhibits anti-tumor activity in vitro and in vivo

<p>Abstract</p> <p>Background</p> <p>Epigallocatechin-3-gallate (EGCG), one of the major catechins in green tea, is a potential chemopreventive agent for various cancers. The aim of this study was to examine the effect of EGCG on the expression of heat shock proteins (HSPs) and tumor suppression.</p> <p>Methods</p> <p>Cell colony formation was evaluated by a soft agar assay. Transcriptional activity of HSP70 and HSP90 was determined by luciferase reporter assay. An EGCG-HSPs complex was prepared using EGCG attached to the cyanogen bromide (CNBr)-activated Sepharose 4B. <it>In vivo </it>effect of EGCG on tumor growth was examined in a xenograft model.</p> <p>Results</p> <p>Treatment with EGCG decreased cell proliferation and colony formation of MCF-7 human breast cancer cells. EGCG specifically inhibited the expression of HSP70 and HSP90 by inhibiting the promoter activity of HSP70 and HSP90. Pretreatment with EGCG increased the stress sensitivity of MCF-7 cells upon heat shock (44°C for 1 h) or oxidative stress (H₂O₂, 500 μM for 24 h). Moreover, treatment with EGCG (10 mg/kg) in a xenograft model resulted in delayed tumor incidence and reduced tumor size, as well as the inhibition of HSP70 and HSP90 expression.</p> <p>Conclusions</p> <p>Overall, these findings demonstrate that HSP70 and HSP90 are potent molecular targets of EGCG and suggest EGCG as a drug candidate for the treatment of human cancer.</p

Differential, Positional-Dependent Transcriptional Response of Antigenic Variation (var) Genes to Biological Stress in Plasmodium falciparum

1% of the genes of the human malaria causing agent Plasmodium falciparum belong to the heterogeneous var gene family which encodes P. falciparum erythrocyte membrane protein 1 (PFEMP1). This protein mediates part of the pathogenesis of the disease by causing adherence of infected erythrocytes (IE) to the host endothelium. At any given time, only one copy of the family is expressed on the IE surface. The cues which regulate the allelic exclusion of these genes are not known. We show the existence of a differential expression pattern of these genes upon exposure to biological stress in relation to their positional placement on the chromosome – expression of centrally located var genes is induced while sub-telomeric copies of the family are repressed - this phenomenon orchestrated by the histone deacetylase pfsir2. Moreover, stress was found to cause a switch in the pattern of the expressed var genes thus acting as a regulatory cue. By using pharmacological compounds which putatively affect pfsir2 activity, distinct changes of var gene expression patterns were achieved which may have therapeutic ramifications. As disease severity is partly associated with expression of particular var gene subtypes, manipulation of the IE environment may serve as a mechanism to direct transcription towards less virulent genes

Antigenic Variation in Plasmodium falciparum Malaria Involves a Highly Structured Switching Pattern

Many pathogenic bacteria, fungi, and protozoa achieve chronic infection through an immune evasion strategy known as antigenic variation. In the human malaria parasite Plasmodium falciparum, this involves transcriptional switching among members of the var gene family, causing parasites with different antigenic and phenotypic characteristics to appear at different times within a population. Here we use a genome-wide approach to explore this process in vitro within a set of cloned parasite populations. Our analyses reveal a non-random, highly structured switch pathway where an initially dominant transcript switches via a set of switch-intermediates either to a new dominant transcript, or back to the original. We show that this specific pathway can arise through an evolutionary conflict in which the pathogen has to optimise between safeguarding its limited antigenic repertoire and remaining capable of establishing infections in non-naïve individuals. Our results thus demonstrate a crucial role for structured switching during the early phases of infections and provide a unifying theory of antigenic variation in P. falciparum malaria as a balanced process of parasite-intrinsic switching and immune-mediated selection

Default Pathway of var2csa Switching and Translational Repression in Plasmodium falciparum

Antigenic variation is a subtle process of fundamental importance to the survival of a microbial pathogen. In Plasmodium falciparum malaria, PfEMP1 is the major variable antigen and adhesin expressed at the surface of the infected erythrocyte, which is encoded for by members of a family of 60 var-genes. Peri-nuclear repositioning and epigenetic mechanisms control their mono-allelic expression. The switching of PfEMP1 depends in part on variable transition rates and short-lived immune responses to shared minor epitopes. Here we show var-genes to switch to a common gene that is highly transcribed, but sparsely translated into PfEMP1 and not expressed at the erythrocyte surface. Highly clonal and adhesive P. falciparum, which expressed distinct var-genes and the corresponding PfEMP1s at onset, were propagated without enrichment or panning. The parasites successively and spontaneously switched to transcribe a shared var-gene (var2csa) matched by the loss of PfEMP1 surface expression and host cell-binding. The var2csa gene repositioned in the peri-nuclear area upon activation, away from the telomeric clusters and heterochromatin to transcribe spliced, full-length RNA. Despite abundant transcripts, the level of intracellular PfEMP1 was low suggesting post-transcriptional mechanisms to partake in protein expression. In vivo, off-switching and translational repression may constitute one pathway, among others, coordinating PfEMP1 expression

Heat Shock Protein-27, -60 and -90 expression in gastric cancer: association with clinicopathological variables and patient survival

<p>Abstract</p> <p>Background</p> <p>Heat shock proteins (HSPs) are ubiquitous, highly conserved proteins across all the species and play essential roles in maintaining protein stability within the cells under normal conditions, while preventing stress-induced cellular damage. HSPs were also overexpressed in various types of cancer, being associated with tumor cell proliferation, differentiation and apoptosis. The aim of the present study was to evaluate the clinical significance of HSP -27, -60, and -90 expression in gastric carcinoma.</p> <p>Methods</p> <p>HSP -27, -60, and -90 proteins expression was assessed immunohistochemically in tumoral samples of 66 gastric adenocarcinoma patients and was statistically analyzed in relation to various clinicopathological characteristics, tumor proliferative capacity and patients' survival.</p> <p>Results</p> <p>HSP-27, -60, -90 proteins were abundantly expressed in gastric adenocarcinoma cases examined. HSP-27 expression was significantly associated with tumor size (pT, P = 0.026), the presence of organ metastases (pM, P = 0.046) and pStage (P = 0.041), while HSP-27 staining intensity with nodal status (pN, P = 0.042). HSP-60 expression was significantly associated with patients' sex (P = 0.011), while HSP-60 staining intensity with patients' age (P = 0.027) and tumor histopathological grade (P = 0.031). HSP-90 expression was not associated with any of the clinicopathological parameters examined; however, HSP-90 staining intensity was significantly associated with tumor size (pT, P = 0.020). High HSP-90 expression was significantly associated with longer overall survival times in univariate analysis (log-rank test, P = 0.033), being also identified as an independent prognostic factor in multivariate analysis (P = 0.026).</p> <p>Conclusion</p> <p>HSP-27, -60, and -90 were associated with certain clinicopathological parameters which are crucial for the management of gastric adenocarcinoma patient. HSP-90 expression may also be an independent prognostic indicator in gastric adenocarcinoma patients.</p

A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens

Understanding the mechanisms by which plants trigger host defenses in response to viruses has been a challenging problem owing to the multiplicity of factors and complexity of interactions involved. The advent of genomic techniques, however, has opened the possibility to grasp a global picture of the interaction. Here, we used Arabidopsis thaliana to identify and compare genes that are differentially regulated upon infection with seven distinct (+)ssRNA and one ssDNA plant viruses. In the first approach, we established lists of genes differentially affected by each virus and compared their involvement in biological functions and metabolic processes. We found that phylogenetically related viruses significantly alter the expression of similar genes and that viruses naturally infecting Brassicaceae display a greater overlap in the plant response. In the second approach, virus-regulated genes were contextualized using models of transcriptional and protein-protein interaction networks of A. thaliana. Our results confirm that host cells undergo significant reprogramming of their transcriptome during infection, which is possibly a central requirement for the mounting of host defenses. We uncovered a general mode of action in which perturbations preferentially affect genes that are highly connected, central and organized in modules. © 2012 Rodrigo et al.This work was supported by the Spanish Ministerio de Ciencia e Innovacion (MICINN) grants BFU2009-06993 (S. F. E.) and BIO2006-13107 (C. L.) and by Generalitat Valenciana grant PROMETEO2010/016 (S. F. E.). G. R. is supported by a graduate fellowship from the Generalitat Valenciana (BFPI2007-160) and J.C. by a contract from MICINN grant TIN2006-12860. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Rodrigo Tarrega, G.; Carrera Montesinos, J.; Ruiz-Ferrer, V.; Del Toro, F.; Llave, C.; Voinnet, O.; Elena Fito, SF. (2012). A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens. PLoS ONE. 7(7):40526-40526. https://doi.org/10.1371/journal.pone.0040526S405264052677Peng, X., Chan, E. Y., Li, Y., Diamond, D. L., Korth, M. J., & Katze, M. G. (2009). Virus–host interactions: from systems biology to translational research. Current Opinion in Microbiology, 12(4), 432-438. doi:10.1016/j.mib.2009.06.003Dodds, P. N., & Rathjen, J. P. (2010). Plant immunity: towards an integrated view of plant–pathogen interactions. Nature Reviews Genetics, 11(8), 539-548. doi:10.1038/nrg2812Maule, A., Leh, V., & Lederer, C. (2002). The dialogue between viruses and hosts in compatible interactions. Current Opinion in Plant Biology, 5(4), 279-284. doi:10.1016/s1369-5266(02)00272-8Whitham, S. A., Quan, S., Chang, H.-S., Cooper, B., Estes, B., Zhu, T., … Hou, Y.-M. (2003). Diverse RNA viruses elicit the expression of common sets of genes in susceptibleArabidopsis thalianaplants. The Plant Journal, 33(2), 271-283. doi:10.1046/j.1365-313x.2003.01625.xBailer, S., & Haas, J. (2009). Connecting viral with cellular interactomes. Current Opinion in Microbiology, 12(4), 453-459. doi:10.1016/j.mib.2009.06.004Whitham, S. A., Yang, C., & Goodin, M. M. (2006). Global Impact: Elucidating Plant Responses to Viral Infection. Molecular Plant-Microbe Interactions, 19(11), 1207-1215. doi:10.1094/mpmi-19-1207MacPherson, J. I., Dickerson, J. E., Pinney, J. W., & Robertson, D. L. (2010). Patterns of HIV-1 Protein Interaction Identify Perturbed Host-Cellular Subsystems. PLoS Computational Biology, 6(7), e1000863. doi:10.1371/journal.pcbi.1000863Jenner, R. G., & Young, R. A. (2005). Insights into host responses against pathogens from transcriptional profiling. Nature Reviews Microbiology, 3(4), 281-294. doi:10.1038/nrmicro1126Andeweg, A. C., Haagmans, B. L., & Osterhaus, A. D. (2008). Virogenomics: the virus–host interaction revisited. Current Opinion in Microbiology, 11(5), 461-466. doi:10.1016/j.mib.2008.09.010Elena, S. F., Carrera, J., & Rodrigo, G. (2011). A systems biology approach to the evolution of plant–virus interactions. Current Opinion in Plant Biology, 14(4), 372-377. doi:10.1016/j.pbi.2011.03.013Tan, S.-L., Ganji, G., Paeper, B., Proll, S., & Katze, M. G. (2007). Systems biology and the host response to viral infection. Nature Biotechnology, 25(12), 1383-1389. doi:10.1038/nbt1207-1383De la Fuente, A. (2010). From ‘differential expression’ to ‘differential networking’ – identification of dysfunctional regulatory networks in diseases. Trends in Genetics, 26(7), 326-333. doi:10.1016/j.tig.2010.05.001Albert, R. (2005). Scale-free networks in cell biology. Journal of Cell Science, 118(21), 4947-4957. doi:10.1242/jcs.02714Yu, H., Braun, P., Yildirim, M. A., Lemmens, I., Venkatesan, K., Sahalie, J., … Vidal, M. (2008). High-Quality Binary Protein Interaction Map of the Yeast Interactome Network. Science, 322(5898), 104-110. doi:10.1126/science.1158684Barabási, A.-L., & Oltvai, Z. N. (2004). Network biology: understanding the cell’s functional organization. Nature Reviews Genetics, 5(2), 101-113. doi:10.1038/nrg1272Albert, R., Jeong, H., & Barabási, A.-L. (2000). Error and attack tolerance of complex networks. Nature, 406(6794), 378-382. doi:10.1038/35019019Mukhtar, M. S., Carvunis, A.-R., Dreze, M., Epple, P., Steinbrenner, J., … Moore, J. (2011). Independently Evolved Virulence Effectors Converge onto Hubs in a Plant Immune System Network. Science, 333(6042), 596-601. doi:10.1126/science.1203659Calderwood, M. A., Venkatesan, K., Xing, L., Chase, M. R., Vazquez, A., Holthaus, A. M., … Johannsen, E. (2007). Epstein-Barr virus and virus human protein interaction maps. Proceedings of the National Academy of Sciences, 104(18), 7606-7611. doi:10.1073/pnas.0702332104De Chassey, B., Navratil, V., Tafforeau, L., Hiet, M. S., Aublin‐Gex, A., Agaugué, S., … Lotteau, V. (2008). Hepatitis C virus infection protein network. Molecular Systems Biology, 4(1), 230. doi:10.1038/msb.2008.66Shapira, S. D., Gat-Viks, I., Shum, B. O. V., Dricot, A., de Grace, M. M., Wu, L., … Hacohen, N. (2009). A Physical and Regulatory Map of Host-Influenza Interactions Reveals Pathways in H1N1 Infection. Cell, 139(7), 1255-1267. doi:10.1016/j.cell.2009.12.018Dyer, M. D., Murali, T. M., & Sobral, B. W. (2008). The Landscape of Human Proteins Interacting with Viruses and Other Pathogens. PLoS Pathogens, 4(2), e32. doi:10.1371/journal.ppat.0040032Golem, S., & Culver, J. N. (2003). Tobacco mosaic virusInduced Alterations in the Gene Expression Profile ofArabidopsis thaliana. Molecular Plant-Microbe Interactions, 16(8), 681-688. doi:10.1094/mpmi.2003.16.8.681Espinoza, C., Medina, C., Somerville, S., & Arce-Johnson, P. (2007). Senescence-associated genes induced during compatible viral interactions with grapevine and Arabidopsis. Journal of Experimental Botany, 58(12), 3197-3212. doi:10.1093/jxb/erm165Yang, C., Guo, R., Jie, F., Nettleton, D., Peng, J., Carr, T., … Whitham, S. A. (2007). Spatial Analysis ofArabidopsis thalianaGene Expression in Response toTurnip mosaic virusInfection. Molecular Plant-Microbe Interactions, 20(4), 358-370. doi:10.1094/mpmi-20-4-0358Agudelo-Romero, P., Carbonell, P., de la Iglesia, F., Carrera, J., Rodrigo, G., Jaramillo, A., … Elena, S. F. (2008). Changes in the gene expression profile of Arabidopsis thaliana after infection with Tobacco etch virus. Virology Journal, 5(1), 92. doi:10.1186/1743-422x-5-92Agudelo-Romero, P., Carbonell, P., Perez-Amador, M. A., & Elena, S. F. (2008). Virus Adaptation by Manipulation of Host’s Gene Expression. PLoS ONE, 3(6), e2397. doi:10.1371/journal.pone.0002397Ascencio-Ibáñez, J. T., Sozzani, R., Lee, T.-J., Chu, T.-M., Wolfinger, R. D., Cella, R., & Hanley-Bowdoin, L. (2008). Global Analysis of Arabidopsis Gene Expression Uncovers a Complex Array of Changes Impacting Pathogen Response and Cell Cycle during Geminivirus Infection. Plant Physiology, 148(1), 436-454. doi:10.1104/pp.108.121038Babu, M., Griffiths, J. S., Huang, T.-S., & Wang, A. (2008). Altered gene expression changes in Arabidopsis leaf tissues and protoplasts in response to Plum pox virus infection. BMC Genomics, 9(1), 325. doi:10.1186/1471-2164-9-325De Vienne, D. M., Giraud, T., & Martin, O. C. (2007). A congruence index for testing topological similarity between trees. Bioinformatics, 23(23), 3119-3124. doi:10.1093/bioinformatics/btm500Wise, R. P., Moscou, M. J., Bogdanove, A. J., & Whitham, S. A. (2007). Transcript Profiling in Host–Pathogen Interactions. Annual Review of Phytopathology, 45(1), 329-369. doi:10.1146/annurev.phyto.45.011107.143944Handford, M. G., & Carr, J. P. (2007). A defect in carbohydrate metabolism ameliorates symptom severity in virus-infected Arabidopsis thaliana. Journal of General Virology, 88(1), 337-341. doi:10.1099/vir.0.82376-0Hou, B., Lim, E.-K., Higgins, G. S., & Bowles, D. J. (2004). N-Glucosylation of Cytokinins by Glycosyltransferases ofArabidopsis thaliana. Journal of Biological Chemistry, 279(46), 47822-47832. doi:10.1074/jbc.m409569200Schwender, J., Goffman, F., Ohlrogge, J. B., & Shachar-Hill, Y. (2004). Rubisco without the Calvin cycle improves the carbon efficiency of developing green seeds. Nature, 432(7018), 779-782. doi:10.1038/nature03145Pagán, I., Alonso-Blanco, C., & García-Arenal, F. (2008). Host Responses in Life-History Traits and Tolerance to Virus Infection in Arabidopsis thaliana. PLoS Pathogens, 4(8), e1000124. doi:10.1371/journal.ppat.1000124Carrera, J., Rodrigo, G., Jaramillo, A., & Elena, S. F. (2009). Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions. Genome Biology, 10(9), R96. doi:10.1186/gb-2009-10-9-r96Geisler-Lee, J., O’Toole, N., Ammar, R., Provart, N. J., Millar, A. H., & Geisler, M. (2007). A Predicted Interactome for Arabidopsis. Plant Physiology, 145(2), 317-329. doi:10.1104/pp.107.103465Ma, S., Gong, Q., & Bohnert, H. J. (2007). An Arabidopsis gene network based on the graphical Gaussian model. Genome Research, 17(11), 1614-1625. doi:10.1101/gr.6911207Yamada, T., & Bork, P. (2009). Evolution of biomolecular networks — lessons from metabolic and protein interactions. Nature Reviews Molecular Cell Biology, 10(11), 791-803. doi:10.1038/nrm2787Humphries, M. D., & Gurney, K. (2008). Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence. PLoS ONE, 3(4), e0002051. doi:10.1371/journal.pone.0002051Stumpf, M. P. H., & Ingram, P. J. (2005). Probability models for degree distributions of protein interaction networks. Europhysics Letters (EPL), 71(1), 152-158. doi:10.1209/epl/i2004-10531-8Khanin, R., & Wit, E. (2006). How Scale-Free Are Biological Networks. Journal of Computational Biology, 13(3), 810-818. doi:10.1089/cmb.2006.13.810Daudin, J.-J., Picard, F., & Robin, S. (2007). A mixture model for random graphs. Statistics and Computing, 18(2), 173-183. doi:10.1007/s11222-007-9046-7Uetz, P. (2006). Herpesviral Protein Networks and Their Interaction with the Human Proteome. Science, 311(5758), 239-242. doi:10.1126/science.1116804Choi, I.-R., Stenger, D. C., & French, R. (2000). Multiple Interactions among Proteins Encoded by the Mite-Transmitted Wheat Streak Mosaic Tritimovirus. Virology, 267(2), 185-198. doi:10.1006/viro.1999.0117Guo, D., Saarma, M., Rajamäki, M.-L., & Valkonen, J. P. T. (2001). Towards a protein interaction map of potyviruses: protein interaction matrixes of two potyviruses based on the yeast two-hybrid system. Journal of General Virology, 82(4), 935-939. doi:10.1099/0022-1317-82-4-935Lin, L., Shi, Y., Luo, Z., Lu, Y., Zheng, H., Yan, F., … Wu, Y. (2009). Protein–protein interactions in two potyviruses using the yeast two-hybrid system. Virus Research, 142(1-2), 36-40. doi:10.1016/j.virusres.2009.01.006Shen, W., Wang, M., Yan, P., Gao, L., & Zhou, P. (2010). Protein interaction matrix of Papaya ringspot virus type P based on a yeast two-hybrid system. Acta Virologica, 54(1), 49-54. doi:10.4149/av_2010_01_49Redner, S. (2008). Teasing out the missing links. Nature, 453(7191), 47-48. doi:10.1038/453047aIrizarry, R. A. (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4(2), 249-264. doi:10.1093/biostatistics/4.2.249Smyth, G. K. (2004). Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. Statistical Applications in Genetics and Molecular Biology, 3(1), 1-25. doi:10.2202/1544-6115.1027Allemeersch, J., Durinck, S., Vanderhaeghen, R., Alard, P., Maes, R., Seeuws, K., … Kuiper, M. T. R. (2005). Benchmarking the CATMA Microarray. A Novel Tool forArabidopsis Transcriptome Analysis. Plant Physiology, 137(2), 588-601. doi:10.1104/pp.104.051300Cleveland, W. S. (1979). Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Association, 74(368), 829-836. doi:10.1080/01621459.1979.10481038Tarraga, J., Medina, I., Carbonell, J., Huerta-Cepas, J., Minguez, P., Alloza, E., … Dopazo, J. (2008). GEPAS, a web-based tool for microarray data analysis and interpretation. Nucleic Acids Research, 36(Web Server), W308-W314. doi:10.1093/nar/gkn303Al-Shahrour, F., Minguez, P., Vaquerizas, J. M., Conde, L., & Dopazo, J. (2005). BABELOMICS: a suite of web tools for functional annotation and analysis of groups of genes in high-throughput experiments. Nucleic Acids Research, 33(Web Server), W460-W464. doi:10.1093/nar/gki456Al-Shahrour, F., Minguez, P., Tárraga, J., Medina, I., Alloza, E., Montaner, D., & Dopazo, J. (2007). FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments. Nucleic Acids Research, 35(suppl_2), W91-W96. doi:10.1093/nar/gkm260Mueller, L. A., Zhang, P., & Rhee, S. Y. (2003). AraCyc: A Biochemical Pathway Database for Arabidopsis. Plant Physiology, 132(2), 453-460. doi:10.1104/pp.102.017236Navratil, V., de Chassey, B., Combe, C., & Lotteau, V. (2011). When the human viral infectome and diseasome networks collide: towards a systems biology platform for the aetiology of human diseases. BMC Systems Biology, 5(1), 13. doi:10.1186/1752-0509-5-13Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423. doi:10.1002/j.1538-7305.1948.tb01338.

Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens

Abstract Background In the course of infection, viruses such as HIV-1 must enter a cell, travel to sites where they can hijack host machinery to transcribe their genes and translate their proteins, assemble, and then leave the cell again, all while evading the host immune system. Thus, successful infection depends on the pathogen's ability to manipulate the biological pathways and processes of the organism it infects. Interactions between HIV-encoded and human proteins provide one means by which HIV-1 can connect into cellular pathways to carry out these survival processes. Results We developed and applied a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence. Conclusions Understanding the interplay between HIV-1 and its human host will help in understanding the viral lifecycle and the ways in which this virus is able to manipulate its host. The results shown here provide a potential set of interactions that are amenable to further experimental manipulation as well as potential targets for therapeutic intervention

From In Vivo to In Vitro: Dynamic Analysis of Plasmodium falciparum var Gene Expression Patterns of Patient Isolates during Adaptation to Culture

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), encoded by the var gene family, plays a crucial role in disease virulence through its involvement in binding to various host cellular receptors during infection. Growing evidence suggests that differential expression of the various var subgroups may be involved in parasite virulence. To further explore this issue, we have collected isolates from symptomatic patients in south China-Myanmar border, and characterized their sequence diversity and transcription profiles over time of var gene family, and cytoadherence properties from the time of their initial collection and extending through a two month period of adaptation to culture. Initially, we established a highly diverse, DBLα (4 cysteines) subtype-enriched, but unique local repertoire of var-DBL1α sequences by cDNA cloning and sequencing. Next we observed a rapid transcriptional decline of upsA- and upsB-subtype var genes at ring stage through qRT-PCR assays, and a switching event from initial ICAM-I binding to the CD36-binding activity during the first week of adaptive cultivation in vitro. Moreover, predominant transcription of upsA var genes was observed to be correlated with those isolates that showed a higher parasitemia at the time of collection and the ICAM-1-binding phenotype in culture. Taken together, these data indicate that the initial stage of adaptive process in vitro significantly influences the transcription of virulence-related var subtypes and expression of PfEMP1 variants. Further, the specific upregulation of the upsA var genes is likely linked to the rapid propagation of the parasite during natural infection due to the A-type PfEMP1 variant-mediated growth advantages

Co-chaperones are limiting in a depleted chaperone network

To probe the limiting nodes in the chaperoning network which maintains cellular proteostasis, we expressed a dominant negative mutant of heat shock factor 1 (dnHSF1), the regulator of the cytoplasmic proteotoxic stress response. Microarray analysis of non-stressed dnHSF1 cells showed a two- or more fold decrease in the transcript level of 10 genes, amongst which are the (co-)chaperone genes HSP90AA1, HSPA6, DNAJB1 and HSPB1. Glucocorticoid signaling, which requires the Hsp70 and the Hsp90 folding machines, was severely impaired by dnHSF1, but fully rescued by expression of DNAJA1 or DNAJB1, and partially by ST13. Expression of DNAJB6, DNAJB8, HSPA1A, HSPB1, HSPB8, or STIP1 had no effect while HSP90AA1 even inhibited. PTGES3 (p23) inhibited only in control cells. Our results suggest that the DNAJ co-chaperones in particular become limiting in a depleted chaperoning network. Our results also suggest a difference between the transcriptomes of cells lacking HSF1 and cells expressing dnHSF1