BBBomics-human blood brain barrier transcriptomics hub

Frontiers in Neuroscience

Volumn 10, Issue MAR, 2016, Pages

  Kalari, Krishna R ^a   Thompson, Kevin J ^a   Nair, Asha A ^a   Tang, Xiaojia ^a   Bockol, Matthew A ^a   Jhawar, Navya ^a   Swaminathan, Suresh K ^b   Lowe, Val J ^a   Kandimalla, Karunya K ^b  

^a MAYO CLINIC   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

Author keywords

BBB database; Blood brain barrier; Informatics analysis; MicroRNA seq; Pathways; RNA seq; Transcriptomics

Indexed keywords

CIRCRNA; LONG UNTRANSLATED RNA; MESSENGER RNA; MICRORNA; NUCLEOTIDE; UNCLASSIFIED DRUG; UNTRANSLATED RNA;

ACCESS TO INFORMATION; ALTERNATIVE RNA SPLICING; ARTICLE; BBBOMICS; BLOOD BRAIN BARRIER; CONTROLLED STUDY; DATA BASE; GENE EXPRESSION; GENETIC CODE; GENETIC VARIABILITY; HUMAN; HUMAN CELL; RNA SEQUENCE; TRANSCRIPTOMICS;

EID: 84964335870     PISSN: 16624548     EISSN: 1662453X     Source Type: Journal    
DOI: 10.3389/fnins.2016.00071     Document Type: Article

References (31)

1. Agyare, E. K., Leonard, S. R., Curran, G. L., Yu, C. C., Lowe, V. J., Paravastu, A. K., et al. (2013). Traffic jam at the blood-brain barrier promotes greater accumulation of Alzheimer's disease amyloid-beta proteins in the cerebral vasculature. Mol. Pharm. 10, 1557-1565. doi: 10.1021/mp300352c
2. An, J., Lai, J., Lehman, M. L., and Nelson, C. C. (2013). miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res. 41, 727-737. doi: 10.1093/nar/gks1187
3. Bamji-Mirza, M., Callaghan, D., Najem, D., Shen, S., Hasim, M. S., Yang, Z., et al. (2014). Stimulation of insulin signaling and inhibition of JNK-AP1 activation protect cells from amyloid-beta-induced signaling dysregulation and inflammatory response. J. Alzheimers Dis. 40, 105-122. doi: 10.3233/JAD-131949
4. Carmeliet, P., and De Strooper, B. (2012). Alzheimer's disease: a breach in the blood-brain barrier. Nature 485, 451-452. doi: 10.1038/485451a
5. Friedman, R. C., Farh, K. K., Burge, C. B., and Bartel, D. P. (2009). Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 19, 92-105. doi: 10.1101/gr.082701.108
6. Hansen, K. D., Irizarry, R. A., and Wu, Z. (2012). Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics 13, 204-216. doi: 10.1093/biostatistics/kxr054
7. Ilina, P., Partti, S., Niklander, J., Ruponen, M., Lou, Y. R., and Yliperttula, M. (2015). Effect of differentiation on endocytic profiles of endothelial and epithelial cell culture models. Exp. Cell Res. 332, 89-101. doi: 10.1016/j.yexcr.2015.01.002
8. Kalari, K. R., Nair, A. A., Bhavsar, J. D., O'Brien, D. R., Davila, J. I., Bockol, M. A., et al. (2014). MAP-RSeq: mayo analysis pipeline for RNA sequencing. BMC Bioinformatics 15:224. doi: 10.1186/1471-2105-15-224
9. Karolchik, D., Barber, G. P., Casper, J., Clawson, H., Cline, M. S., Diekhans, M., et al. (2014). The UCSC genome browser database: 2014 update. Nucleic Acids Res. 42, D764-D770. doi: 10.1093/nar/gkt1168
10. Katz, Y., Wang, E. T., Airoldi, E. M., and Burge, C. B. (2010). Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7, 1009-1015. doi: 10.1038/nmeth.1528
11. Kent, W. J. (2002). BLAT-the BLAST-like alignment tool. Genome Res. 12, 656-664. doi: 10.1101/gr.229202
12. Kortekaas, R., Leenders, K. L., van Oostrom, J. C., Vaalburg, W., Bart, J., Willemsen, A. T., et al. (2005). Blood-brain barrier dysfunction in parkinsonian midbrain in vivo. Ann. Neurol. 57, 176-179. doi: 10.1002/ana.20369
13. Kozomara, A., and Griffiths-Jones, S. (2011). miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 39, D152-D157. doi: 10.1093/nar/gkq1027
14. Langmead, B., and Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357-359. doi: 10.1038/nmeth.1923
15. Lopez-Ramirez, M. A., Male, D. K., Wang, C., Sharrack, B., Wu, D., and Romero, I. A. (2013). Cytokine-induced changes in the gene expression profile of a human cerebral microvascular endothelial cell-line, hCMEC/D3. Fluids Barriers CNS 10:27. doi: 10.1186/2045-8118-10-27
16. Lopez-Ramirez, M. A., Wu, D., Pryce, G., Simpson, J. E., Reijerkerk, A., King-Robson, J., et al. (2014). MicroRNA-155 negatively affects blood-brain barrier function during neuroinflammation. FASEB J. 28, 2551-2565. doi: 10.1096/fj.13-248880
17. Luo, W., and Brouwer, C. (2013). Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics 29, 1830-1831. doi: 10.1093/bioinformatics/btt285
18. Naik, P., Sajja, R. K., Prasad, S., and Cucullo, L. (2015). Effect of full flavor and denicotinized cigarettes exposure on the brain microvascular endothelium: a microarray-based gene expression study using a human immortalized BBB endothelial cell line. BMC Neurosci. 16:38. doi: 10.1186/s12868-015-0173-3
19. Pertea, M., Pertea, G. M., Antonescu, C. M., Chang, T. C., Mendell, J. T., and Salzberg, S. L. (2015). StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290-295. doi: 10.1038/nbt.3122
20. Poller, B., Gutmann, H., Krahenbuhl, S., Weksler, B., Romero, I., Couraud, P. O., et al. (2008). The human brain endothelial cell line hCMEC/D3 as a human blood-brain barrier model for drug transport studies. J. Neurochem. 107, 1358-1368. doi: 10.1111/j.1471-4159.2008.05730.x
21. Quinlan, A. R., and Hall, I. M. (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841-842. doi: 10.1093/bioinformatics/btq033
22. Sajja, R. K., and Cucullo, L. (2015). Altered glycaemia differentially modulates efflux transporter expression and activity in hCMEC/D3 cell line. Neurosci. Lett. 598, 59-65. doi: 10.1016/j.neulet.2015.05.015
23. Sun, K., Chen, X., Jiang, P., Song, X., Wang, H., and Sun, H. (2013). iSeeRNA: identification of long intergenic non-coding RNA transcripts from transcriptome sequencing data. BMC Genomics 14:S7. doi: 10.1186/1471-2164-14-S2-S7
24. Sun, Z., Evans, J., Bhagwate, A., Middha, S., Bockol, M., Yan, H., et al. (2014). CAP-miRSeq: a comprehensive analysis pipeline for microRNA sequencing data. BMC Genomics 15:423. doi: 10.1186/1471-2164-15-423
25. Tang, X., Baheti, S., Shameer, K., Thompson, K. J., Wills, Q., Niu, N., et al. (2014). The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data. Nucleic Acids Res. 42:e172. doi: 10.1093/nar/gku1005
26. Team, B. (2015). Homo.sapiens: Annotation Package for the Homo.sapiens Object Bioconductor. Seattle, WA: The Fred Hutchinson Cancer Research Center
27. Tenenbaum, D. (2015). KEGGREST: Client-Side REST Access to KEGG, Bioconductor. Seattle, WA: The Fred Hutchinson Cancer Research Center
28. Urich, E., Lazic, S. E., Molnos, J., Wells, I., and Freskgard, P. O. (2012). Transcriptional profiling of human brain endothelial cells reveals key properties crucial for predictive in vitro blood-brain barrier models. PLoS ONE 7:e38149. doi: 10.1371/journal.pone.0038149
29. Vu, K., Weksler, B., Romero, I., Couraud, P. O., and Gelli, A. (2009). Immortalized human brain endothelial cell line HCMEC/D3 as a model of the blood-brain barrier facilitates in vitro studies of central nervous system infection by Cryptococcus neoformans. Eukaryotic Cell 8, 1803-1807. doi: 10.1128/EC.00240-09
30. Weksler, B., Romero, I. A., and Couraud, P. O. (2013). The hCMEC/D3 cell line as a model of the human blood brain barrier. Fluids Barriers CNS 10:16. doi: 10.1186/2045-8118-10-16
31. Yang, Y., and Rosenberg, G. A. (2011). Blood-brain barrier breakdown in acute and chronic cerebrovascular disease. Stroke 42, 3323-3328. doi: 10.1161/STROKEAHA.110.608257