Cited by 2,294

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Dynamic regulation of genome-wide pre-mRNA splicing and stress tolerance by the Sm-like protein LSm5 in Arabidopsis

Open access

Cui, Peng, Zhang, Shoudong, Ding, Feng, Ali, Shahjahan, Xiong, Liming·2014·Genome Biology

DOI 10.1186/gb-2014-15-1-r1

Open this paper in CoChat — free account

Abstract

Background: Sm-like proteins are highly conserved proteins that form the core of the U6 ribonucleoprotein and function in several mRNA metabolism processes, including pre-mRNA splicing. Despite their wide occurrence in all eukaryotes, little is known about the roles of Sm-like proteins in the regulation of splicing. Results: Here, through comprehensive transcriptome analyses, we demonstrate that depletion of the Arabidopsis supersensitive to abscisic acid and drought 1 gene ( SAD1 ), which encodes Sm-like protein 5 (LSm5), promotes an inaccurate selection of splice sites that leads to a genome-wide increase in alternative splicing. In contrast, overexpression of SAD1 strengthens the precision of splice-site recognition and globally inhibits alternative splicing. Further, SAD1 modulates the splicing of stress-responsive genes, particularly under salt-stress conditions. Finally, we find that overexpression of SAD1 in Arabidopsis improves salt tolerance in transgenic plants, which correlates with an increase in splicing accuracy and efficiency for stress-responsive genes. Conclusions: We conclude that SAD1 dynamically controls splicing efficiency and splice-site recognition in Arabidopsis , and propose that this may contribute to SAD1 -mediated stress tolerance through the metabolism of transcripts expressed from stress-responsive genes. Our study not only provides novel insights into the function of Sm-like proteins in splicing, but also uncovers new means to improve splicing efficiency and to enhance stress tolerance in a higher eukaryote.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1186/gb-2014-15-1-r1

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
CIPK3 , a Calcium Sensor–Associated Protein Kinase That Regulates Abscisic Acid and Cold Signal Transduction in Arabidopsis
Kim, K.-N., Cheong, Y. H., Grant, J. J., Pandey, G. K., & Luan, S. (2003) · The Plant Cell · doi:10.1105/tpc.006858 · Cited by 406

Show all 3 references

Research works printed but not linked: 45

Braunschweig U, Gueroussov S, Plocik AM, Graveley BR, Blencowe BJ: Dynamic integration of splicing within gene regulatory pathways. Cell. 2013, 152: 1252-1269. 10.1016/j.cell.2013.02.034. · DOI · Paper page
Faustino NA, Cooper TA: Pre-mRNA splicing and human disease. Genes Dev. 2003, 17: 419-437. 10.1101/gad.1048803. · DOI · Paper page
Singh RK, Cooper TA: Pre-mRNA splicing in disease and therapeutics. Trends Mol Med. 2012, 18: 472-482. 10.1016/j.molmed.2012.06.006. · DOI
Mazzucotelli A, Ribet C, Castan-Laurell I, Daviaud D, Guigne C, Langin D, Valet P: The transcriptional co-activator PGC-1alpha up regulates apelin in human and mouse adipocytes. Regul Pept. 2008, 150: 33-37. 10.1016/j.regpep.2008.04.003. · DOI
Palusa SG, Ali GS, Reddy AS: Alternative splicing of pre-mRNAs of Arabidopsis serine/arginine-rich proteins: regulation by hormones and stresses. Plant J. 2007, 49: 1091-1107. 10.1111/j.1365-313X.2006.03020.x. · DOI
Syed NH, Kalyna M, Marquez Y, Barta A, Brown JW: Alternative splicing in plants–coming of age. Trends Plant Sci. 2012, 17: 616-623. 10.1016/j.tplants.2012.06.001. · DOI
Beggs JD: LSm proteins and RNA processing. Biochem Soc Trans. 2005, 33: 433-438. · DOI
Collins BM, Harrop SJ, Kornfeld GD, Dawes IW, Curmi PM, Mabbutt BC: Crystal structure of a heptameric Sm-like protein complex from archaea: implications for the structure and evolution of snRNPs. J Mol Biol. 2001, 309: 915-923. 10.1006/jmbi.2001.4693. · DOI
Khusial P, Plaag R, Zieve GW: LSm proteins form heptameric rings that bind to RNA via repeating motifs. Trends Biochem Sci. 2005, 30: 522-528. 10.1016/j.tibs.2005.07.006. · DOI
Fenger-Gron M, Fillman C, Norrild B, Lykke-Andersen J: Multiple processing body factors and the ARE binding protein TTP activate mRNA decapping. Mol Cell. 2005, 20: 905-915. 10.1016/j.molcel.2005.10.031. · DOI
Ingelfinger D, Arndt-Jovin DJ, Luhrmann R, Achsel T: The human LSm1-7 proteins colocalize with the mRNA-degrading enzymes Dcp1/2 and Xrn1 in distinct cytoplasmic foci. RNA. 2002, 8: 1489-1501. · DOI
Mayes AE, Verdone L, Legrain P, Beggs JD: Characterization of Sm-like proteins in yeast and their association with U6 snRNA. EMBO J. 1999, 18: 4321-4331. 10.1093/emboj/18.15.4321. · DOI
Verdone L, Galardi S, Page D, Beggs JD: LSm proteins promote regeneration of pre-mRNA splicing activity. Curr Biol. 2004, 14: 1487-1491. 10.1016/j.cub.2004.08.032. · DOI
Xiong L, Gong Z, Rock CD, Subramanian S, Guo Y, Xu W, Galbraith D, Zhu JK: Modulation of abscisic acid signal transduction and biosynthesis by an Sm-like protein in Arabidopsis. Dev Cell. 2001, 1: 771-781. 10.1016/S1534-5807(01)00087-9. · DOI
Perea-Resa C, Hernandez-Verdeja T, Lopez-Cobollo R, del Mar Castellano M, Salinas J: LSM proteins provide accurate splicing and decay of selected transcripts to ensure normal Arabidopsis development. Plant Cell. 2012, 24: 4930-4947. 10.1105/tpc.112.103697. · DOI
Golisz A, Sikorski PJ, Kruszka K, Kufel J: Arabidopsis thaliana LSM proteins function in mRNA splicing and degradation. Nucleic Acids Res. 2013, 41: 6232-6249. 10.1093/nar/gkt296. · DOI
Ishitani M, Xiong LM, Stevenson B, Zhu JK: Genetic analysis of osmotic and cold stress signal transduction in Arabidopsis: interactions and convergence of abscisic acid-dependent and abscisic acid-independent pathways. Plant Cell. 1997, 9: 1935-1949. · DOI
Thorvaldsdottir H, Robinson JT, Mesirov JP: Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform. 2012, 14: 178-192. · DOI · Paper page
Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP: Integrative genomics viewer. Nat Biotechnol. 2011, 29: 24-26. 10.1038/nbt.1754. · DOI · Paper page
Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009, 25: 1105-1111. 10.1093/bioinformatics/btp120. · DOI · Paper page
Huang DW, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4: 44-57. · DOI · Paper page
Huang DW, Sherman BT, Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009, 37: 1-13. 10.1093/nar/gkn923. · DOI · Paper page
Zimmermann P, Hirsch-Hoffmann M, Hennig L, Gruissem W: GENEVESTIGATOR. Arabidopsis microarray database and analysis toolbox. Plant Physiol. 2004, 136: 2621-2632. 10.1104/pp.104.046367. · DOI
Thimm O, Blasing O, Gibon Y, Nagel A, Meyer S, Kruger P, Selbig J, Muller LA, Rhee SY, Stitt M: MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 2004, 37: 914-939. 10.1111/j.1365-313X.2004.02016.x. · DOI · Paper page
Fujii H, Verslues PE, Zhu JK: Arabidopsis decuple mutant reveals the importance of SnRK2 kinases in osmotic stress responses in vivo. Proc Natl Acad Sci USA. 2011, 108: 1717-1722. 10.1073/pnas.1018367108. · DOI · Paper page
Liu JP, Ishitani M, Halfter U, Kim CS, Zhu JK: The Arabidopsis thaliana SOS2 gene encodes a protein kinase that is required for salt tolerance. Proc Natl Acad Sci USA. 2000, 97: 3730-3734. 10.1073/pnas.97.7.3730. · DOI
Sakuma Y, Maruyama K, Osakabe Y, Qin F, Seki M, Shinozaki K, Yamaguchi-Shinozaki K: Functional analysis of an Arabidopsis transcription factor, DREB2A, involved in drought-responsive gene expression. Plant Cell. 2006, 18: 1292-1309. 10.1105/tpc.105.035881. · DOI
Apse MP, Aharon GS, Snedden WA, Blumwald E: Salt tolerance conferred by overexpression of a vacuolar Na+/H+ antiport in Arabidopsis. Science. 1999, 285: 1256-1258. 10.1126/science.285.5431.1256. · DOI
Lai Z, Li Y, Wang F, Cheng Y, Fan B, Yu JQ, Chen Z: Arabidopsis sigma factor binding proteins are activators of the WRKY33 transcription factor in plant defense. Plant Cell. 2011, 23: 3824-3841. 10.1105/tpc.111.090571. · DOI · Paper page
Jiang YQ, Deyholos MK: Functional characterization of Arabidopsis NaCl-inducible WRKY25 and WRKY33 transcription factors in abiotic stresses. Plant Mol Biol. 2009, 69: 91-105. 10.1007/s11103-008-9408-3. · DOI
Koiwa H, Li F, McCully MG, Mendoza I, Koizumi N, Manabe Y, Nakagawa Y, Zhu J, Rus A, Pardo JM, Bressan RA, Hasegawa PM: The STT3a subunit isoform of the Arabidopsis oligosaccharyltransferase controls adaptive responses to salt/osmotic stress. Plant Cell. 2003, 15: 2273-2284. 10.1105/tpc.013862. · DOI
Cheng NH, Pittman JK, Zhu JK, Hirschi KD: The protein kinase SOS2 activates the Arabidopsis H+/Ca2+ antiporter CAX1 to integrate calcium transport and salt tolerance. J Biol Chem. 2004, 279: 2922-2926. · DOI · Paper page
Mitsuya S, Taniguchi M, Miyake H, Takabe T: Disruption of RCI2A leads to over-accumulation of Na+ and increased salt sensitivity in Arabidopsis thaliana plants. Planta. 2005, 222: 1001-1009. 10.1007/s00425-005-0043-9. · DOI
Kim S, Kang JY, Cho DI, Park JH, Kim SY: ABF2, an ABRE-binding bZIP factor, is an essential component of glucose signaling and its overexpression affects multiple stress tolerance. Plant J. 2004, 40: 75-87. 10.1111/j.1365-313X.2004.02192.x. · DOI · Paper page
Yoshida T, Fujita Y, Sayama H, Kidokoro S, Maruyama K, Mizoi J, Shinozaki K, Yamaguchi-Shinozaki K: AREB1, AREB2, and ABF3 are master transcription factors that cooperatively regulate ABRE-dependent ABA signaling involved in drought stress tolerance and require ABA for full activation. Plant J. 2010, 61: 672-685. 10.1111/j.1365-313X.2009.04092.x. · DOI · Paper page
de la Mata M, Alonso CR, Kadener S, Fededa JP, Blaustein M, Pelisch F, Cramer P, Bentley D, Kornblihtt AR: A slow RNA polymerase II affects alternative splicing in vivo. Mol Cell. 2003, 12: 525-532. 10.1016/j.molcel.2003.08.001. · DOI · Paper page
Kornblihtt AR: Coupling transcription and alternative splicing. Adv Exp Med Biol. 2007, 623: 175-189. 10.1007/978-0-387-77374-2_11. · DOI
Xiong LM, Schumaker KS, Zhu JK: Cell signaling during cold, drought, and salt stress. Plant Cell. 2002, 14: S165-S183. 10.1105/tpc.010278. · DOI · Paper page
Yamaguchi-Shinozaki K, Shinozaki K: Transcriptional regulatory networks in cellular responses and tolerance to dehydration and cold stresses. Annu Rev Plant Physiol Plant Mol Biol. 2006, 57: 781-803. · DOI
Hugouvieux V, Kwak JM, Schroeder JI: An mRNA cap binding protein, ABH1, modulates early abscisic acid signal transduction in Arabidopsis. Cell. 2001, 106: 477-487. 10.1016/S0092-8674(01)00460-3. · DOI
Gong ZZ, Dong CH, Lee H, Zhu JH, Xiong LM, Gong DM, Stevenson B, Zhu JK: A DEAD box RNA helicase is essential for mRNA export and important for development and stress responses in Arabidopsis. Plant Cell. 2005, 17: 256-267. 10.1105/tpc.104.027557. · DOI
Guan QM, Wu JM, Zhang YY, Jiang CH, Liu RY, Chai CL, Zhu JH: A DEAD box RNA helicase is critical for pre-mRNA splicing, cold-responsive gene regulation, and cold tolerance in Arabidopsis. Plant Cell. 2013, 25: 342-356. 10.1105/tpc.112.108340. · DOI
Moller IS, Gilliham M, Jha D, Mayo GM, Roy SJ, Coates JC, Haseloff J, Tester M: Shoot Na+ exclusion and increased salinity tolerance engineered by cell type-specific alteration of Na+ transport in Arabidopsis. Plant Cell. 2009, 21: 2163-2178. 10.1105/tpc.108.064568. · DOI · Paper page
Shi HZ, Lee BH, Wu SJ, Zhu JK: Overexpression of a plasma membrane Na+/H+ antiporter gene improves salt tolerance in Arabidopsis thaliana. Nature Biotechnol. 2003, 21: 81-85. · DOI · Paper page
Brooks AN, Yang L, Duff MO, Hansen KD, Park JW, Dudoit S, Brenner SE, Graveley BR: Conservation of an RNA regulatory map between Drosophila and mammals. Genome Res. 2011, 21: 193-202. 10.1101/gr.108662.110. · DOI

Whole-organism lineage tracing by combinatorial and cumulative genome editing
McKenna, A., Findlay, G. M., Gagnon, J. A., Horwitz, M. S., Schier, A. F., & Shendure, J. (2016) · Science · doi:10.1126/science.aaf7907 · Cited by 766
Myelin dysfunction drives amyloid-β deposition in models of Alzheimer’s disease
Depp, C., Sun, T., Sasmita, A. O., Spieth, L., Berghoff, S. A., Nazarenko, T., Overhoff, K., Steixner-Kumar, A. A., Subramanian, S., Arinrad, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06120-6 · Cited by 493
CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data
Cairns, J., Freire-Pritchett, P., Wingett, S. W., Várnai, C., Dimond, A., Plagnol, V., Zerbino, D., Schoenfelder, S., Javierre, B.-M., Osborne, C., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0992-2 · Cited by 417
m6A RNA methylation regulates the fate of endogenous retroviruses
Chelmicki, T., Roger, E., Teissandier, A., Dura, M., Bonneville, L., Rucli, S., Dossin, F., Fouassier, C., Lameiras, S., & Bourc’his, D. (2021) · Nature · doi:10.1038/s41586-020-03135-1 · Cited by 307
Transcriptional profiling of human microglia reveals grey–white matter heterogeneity and multiple sclerosis-associated changes
van der Poel, M., Ulas, T., Mizee, M. R., Hsiao, C.-C., Miedema, S. S. M., Adelia, Schuurman, K. G., Helder, B., Tas, S. W., Schultze, J. L., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08976-7 · Cited by 306
DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of enhancers
de Almeida, B. P., Reiter, F., Pagani, M., & Stark, A. (2021) · doi:10.1101/2021.10.05.463203 · Cited by 284
DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers
de Almeida, B. P., Reiter, F., Pagani, M., & Stark, A. (2022) · Nature Genetics · doi:10.1038/s41588-022-01048-5 · Cited by 283
Targeting immune–fibroblast cell communication in heart failure
Amrute, J. M., Luo, X., Penna, V., Yang, S., Yamawaki, T., Hayat, S., Bredemeyer, A., Jung, I.-H., Kadyrov, F. F., Heo, G. S., et al. (2024) · Nature · doi:10.1038/s41586-024-08008-5 · Cited by 339
Engineering salinity tolerance in plants: progress and prospects
Wani, S. H., Kumar, V., Khare, T., Guddimalli, R., Parveda, M., Solymosi, K., Suprasanna, P., & Kavi Kishor, P. B. (2020) · Planta · doi:10.1007/s00425-020-03366-6 · Cited by 233
Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions
Chen, J., Hu, Z., Sun, S., Tan, Q., Wang, Y., Yu, Q., Zong, L., Hong, L., Xiao, J., Shen, T., et al. (2022) · doi:10.1101/2022.08.06.503062 · Cited by 247
Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data
Lause, J., Berens, P., & Kobak, D. (2021) · Genome Biology · doi:10.1186/s13059-021-02451-7 · Cited by 195
STING activation promotes robust immune response and NK cell–mediated tumor regression in glioblastoma models
Berger, G., Knelson, E. H., Jimenez-Macias, J. L., Nowicki, M. O., Han, S., Panagioti, E., Lizotte, P. H., Adu-Berchie, K., Stafford, A., Dimitrakakis, N., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2111003119 · Cited by 179
Pluripotency and the origin of animal multicellularity
(2019) · doi:10.1038/s41586-019-1290-4 · Cited by 146
Osteocyte transcriptome mapping identifies a molecular landscape controlling skeletal homeostasis and susceptibility to skeletal disease
Youlten, S. E., Kemp, J. P., Logan, J. G., Ghirardello, E. J., Sergio, C. M., Dack, M. R. G., Guilfoyle, S. E., Leitch, V. D., Butterfield, N. C., Komla-Ebri, D., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22517-1 · Cited by 133
Ontogeny and Vulnerabilities of Drug-Tolerant Persisters in HER2+ Breast Cancer
Chang, C. A., Jen, J., Jiang, S., Sayad, A., Mer, A. S., Brown, K. R., Nixon, A. M. L., Dhabaria, A., Tang, K. H., Venet, D., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1265 · Cited by 119
Cell-Type Heterogeneity in Adipose Tissue Is Associated with Complex Traits and Reveals Disease-Relevant Cell-Specific eQTLs
Glastonbury, C. A., Couto Alves, A., El-Sayed Moustafa, J. S., & Small, K. S. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.03.025 · Cited by 114
Profiling the genetic determinants of chromatin accessibility with scalable single-cell CRISPR screens
Liscovitch-Brauer, N., Montalbano, A., Deng, J., Méndez-Mancilla, A., Wessels, H.-H., Moss, N. G., Kung, C.-Y., Sookdeo, A., Guo, X., Geller, E., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00902-x · Cited by 107
GAM: a web-service for integrated transcriptional and metabolic network analysis
Sergushichev, A. A., Loboda, A. A., Jha, A. K., Vincent, E. E., Driggers, E. M., Jones, R. G., Pearce, E. J., & Artyomov, M. N. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw266 · Cited by 94
Biologically informed deep learning to query gene programs in single-cell atlases
Lotfollahi, M., Rybakov, S., Hrovatin, K., Hediyeh-zadeh, S., Talavera-López, C., Misharin, A. V., & Theis, F. J. (2023) · Nature Cell Biology · doi:10.1038/s41556-022-01072-x · Cited by 100
Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data
Eling, N., Richard, A. C., Richardson, S., Marioni, J. C., & Vallejos, C. A. (2018) · Cell Systems · doi:10.1016/j.cels.2018.06.011 · Cited by 99
Causal identification of single-cell experimental perturbation effects with CINEMA-OT
Dong, M., Wang, B., Wei, J., de O. Fonseca, A. H., Perry, C. J., Frey, A., Ouerghi, F., Foxman, E. F., Ishizuka, J. J., Dhodapkar, R. M., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-02040-5 · Cited by 94
Haplotype-aware pantranscriptome analyses using spliced pangenome graphs
Sibbesen, J. A., Eizenga, J. M., Novak, A. M., Sirén, J., Chang, X., Garrison, E., & Paten, B. (2023) · Nature Methods · doi:10.1038/s41592-022-01731-9 · Cited by 83
Transcriptome-wide analysis of differential expression in perturbation atlases
Nadig, A., Replogle, J. M., Pogson, A. N., Murthy, M., McCarroll, S. A., Weissman, J. S., Robinson, E. B., & O’Connor, L. J. (2025) · Nature Genetics · doi:10.1038/s41588-025-02169-3 · Cited by 70
The virus-induced cyclic dinucleotide 2′3′-c-di-GMP mediates STING-dependent antiviral immunity in Drosophila
(2023) · doi:10.1016/j.immuni.2023.08.006 · Cited by 66
The HDAC inhibitor CI-994 acts as a molecular memory aid by facilitating synaptic and intracellular communication after learning
Burns, A. M., Farinelli-Scharly, M., Hugues-Ascery, S., Sanchez-Mut, J. V., Santoni, G., & Gräff, J. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2116797119 · Cited by 49
Targeting Oncogene mRNA Translation in B-Cell Malignancies with eFT226, a Potent and Selective Inhibitor of eIF4A
Thompson, P. A., Eam, B., Young, N. P., Fish, S., Chen, J., Barrera, M., Howard, H., Sung, E., Parra, A., Staunton, J., et al. (2021) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-19-0973 · Cited by 50
Differential microRNA expression analyses across two brain regions in Alzheimer’s disease
Dobricic, V., Schilling, M., Schulz, J., Zhu, L.-S., Zhou, C.-W., Fuß, J., Franzenburg, S., Zhu, L.-Q., Parkkinen, L., Lill, C. M., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02108-4 · Cited by 48
Age, sex and Alzheimer’s disease: a longitudinal study of 3xTg-AD mice reveals sex-specific disease trajectories and inflammatory responses mirrored in postmortem brains from Alzheimer’s patients
Barber, A. J., del Genio, C. L., Swain, A. B., Pizzi, E. M., Watson, S. C., Tapiavala, V. N., Zanazzi, G. J., & Gaur, A. B. (2024) · Alzheimer's Research & Therapy · doi:10.1186/s13195-024-01492-x · Cited by 41
Slow release of a synthetic auxin induces formation of adventitious roots in recalcitrant woody plants
Roth, O., Yechezkel, S., Serero, O., Eliyahu, A., Vints, I., Tzeela, P., Carignano, A., Janacek, D. P., Peters, V., Kessel, A., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02065-3 · Cited by 39
CRISPR/Cas9-targeted removal of unwanted sequences from small-RNA sequencing libraries
Hardigan, A. A., Roberts, B. S., Moore, D. E., Ramaker, R. C., Jones, A. L., & Myers, R. M. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz425 · Cited by 37
Evolution of high-molecular-mass hyaluronic acid is associated with subterranean lifestyle
Zhao, Y., Zheng, Z., Zhang, Z., Xu, Y., Hillpot, E., Lin, Y. S., Zakusilo, F. T., Lu, J. Y., Ablaeva, J., Biashad, S. A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43623-2 · Cited by 34
Antisense miR-132 blockade via the AChE-R splice variant mitigates cortical inflammation
Mishra, N., Friedson, L., Hanin, G., Bekenstein, U., Volovich, M., Bennett, E. R., Greenberg, D. S., & Soreq, H. (2017) · Scientific Reports · doi:10.1038/srep42755 · Cited by 29
Deep Nasal Sinus Cavity Microbiota Dysbiosis in Parkinson’s Disease
Pal, G., Ramirez, V., Engen, P. A., Naqib, A., Forsyth, C. B., Green, S. J., Mahdavinia, M., Batra, P. S., Tajudeen, B. A., & Keshavarzian, A. (2021) · npj Parkinson s Disease · doi:10.1038/s41531-021-00254-y · Cited by 28
Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection
Barry, T., Mason, K., Roeder, K., & Katsevich, E. (2024) · Genome Biology · doi:10.1186/s13059-024-03254-2 · Cited by 28
Noncoding RNA-Mediated Regulation of DNA Methylation: Insights into Plant Epigenetic Mechanisms
Ali, S., & Tang, Y. (2025) · Journal of Plant Growth Regulation · doi:10.1007/s00344-024-11462-0 · Cited by 24
Self-citation
Pharmacological Restriction of Genomic Binding Sites Redirects PU.1 Pioneer Transcription Factor Activity
Taylor, S. J., Stauber, J., Bohorquez, O., Tatsumi, G., Kumari, R., Chakraborty, J., Bartholdy, B. A., Schwenger, E., Sundaravel, S., Farahat, A. A., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01911-7 · Cited by 22
Human gain-of-function variants in HNF1A confer protection from diabetes but independently increase hepatic secretion of atherogenic lipoproteins
DeForest, N., Kavitha, B., Hu, S., Isaac, R., Krohn, L., Wang, M., Du, X., De Arruda Saldanha, C., Gylys, J., Merli, E., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100339 · Cited by 21
Identification of pathways modulating vemurafenib resistance in melanoma cells via a genome-wide CRISPR/Cas9 screen
Goh, C. J. H., Wong, J. H., El Farran, C., Tan, B. X., Coffill, C. R., Loh, Y.-H., Lane, D., & Arumugam, P. (2021) · G3 Genes|Genomes|Genetics · doi:10.1093/g3journal/jkaa069 · Cited by 20
Transcription factors instruct DNA methylation patterns in plant reproductive tissues
(2025) · doi:10.1038/s41556-025-01808-5 · Cited by 17
scLong: a billion-parameter foundation model for capturing long-range gene context in single-cell transcriptomics
Bai, D., Mo, S., Zhang, R., Luo, Y., Gao, J., Yang, J. P., Wu, Q., Rahmani, H., Amariuta, T., Grotjahn, D., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69102-y · Cited by 15
Analysis of multi-condition single-cell data with latent embedding multivariate regression
Ahlmann-Eltze, C., & Huber, W. (2025) · Nature Genetics · doi:10.1038/s41588-024-01996-0 · Cited by 15
Targeting the Immune-Fibrosis Axis in Myocardial Infarction and Heart Failure
Amrute, J. M., Luo, X., Penna, V., Bredemeyer, A., Yamawaki, T., Heo, G. S., Shi, S., Koenig, A., Yang, S., Kadyrov, F., et al. (2022) · bioRxiv · doi:10.1101/2022.10.17.512579 · Cited by 14
Epigenetic phase variation in the gut microbiome enhances bacterial adaptation
Ni, M., Junker, K., Liu, Y., Fan, Y., Li, Y., Qiao, W., Zhang, X.-S., Ksiezarek, M., Mead, E. A., Tourancheau, A., et al. (2026) · Cell Host & Microbe · doi:10.1016/j.chom.2026.04.019 · Cited by 13
Tup1 is critical for transcriptional repression in Quiescence in S. cerevisiae
(2022) · doi:10.1371/journal.pgen.1010559 · Cited by 13
Controlling for false discoveries subsequently to large scale one‐way ANOVA testing in proteomics: Practical considerations
Burger, T. (2023) · PROTEOMICS · doi:10.1002/pmic.202200406 · Cited by 10
Dynamic changes in mRNA nucleocytoplasmic localization in the nitrate response of Arabidopsis roots
(2024) · doi:10.1111/pce.15018 · Cited by 9
Defining Cardiac Recovery at Single Cell Resolution
Amrute, J. M., Lai, L., Ma, P., Koenig, A. L., Kamimoto, K., Bredemeyer, A., Shankar, T. S., Kuppe, C., Kadyrov, F. F., Schulte, L. J., et al. (2022) · doi:10.1101/2022.09.11.507463 · Cited by 6
Microglial states associate with lesion dynamics in multiple sclerosis
van den Bosch, A. M. R., Khoo, J. H., Lu, Z., Liang, H., Wever, D., Pu, L., Eggen, B. J. L., Uhlén, M., Smolders, J., Hamann, J., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117538 · Cited by 6
Robust identification of perturbed cell types in single-cell RNA-seq data
Nicol, P. B., Paulson, D., Qian, G., Liu, X. S., Irizarry, R., & Sahu, A. D. (2023) · doi:10.1101/2023.05.06.539326 · Cited by 33
AP2/ERF Transcription Factor Orthologs of the Desert Tree Prosopis cineraria Show Higher Copy Number and DNA-Binding Affinity than Drought-Sensitive Species
(2024) · doi:10.1007/s00344-024-11532-3 · Cited by 3
Rapid photo-crosslinking in living cells reveals protein–nucleic acid dynamics on a timescale of minutes
Trendel, J., Prokofeva, P., Chen, Z. A., Horn, L., Trendel, S., Mema, M., Stuiver, M., Rappsilber, J., & Kuster, B. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag339 · Cited by 3
Microbial hauberks: composition and function of surface layer proteins in gammaproteobacterial methanotrophs
Hamilton, R., Gebbie, W., Bowman, C., Mantanona, A., & Kalyuzhnaya, M. G. (2025) · Applied and Environmental Microbiology · doi:10.1128/aem.01364-24 · Cited by 1
N6-methyladenine DNA modification modulates pathogen virulence in nematodes
Dai, D., Zhang, S., Hu, B., Zhou, Y., Cui, S., Sun, J., Zhang, Y., Wang, X., Siddique, S., Bo, D., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2525035123 · Cited by 1
Self-citation
Detecting Interspecific Positive Selection Using Convolutional Neural Networks
West, C., Walker, C. R., Arasti, S., Vasilev, V., Xu, X., De Maio, N., & Goldman, N. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf154 · Cited by 1
Mechanistic insights into dual-active liver and blood-stage antiplasmodials
(2025) · doi:10.1128/mbio.02423-25

Authors: Cui, P., Zhang, S., Ding, F., Ali, S., & Xiong, L.
Venue: Genome Biology
Publisher: Springer Science and Business Media LLC
Published: 2014-01-07
Volume: 15
Issue: 1
Pages: R1-R1
ISSN: 1465-6906 (ISSN-L)
1474-760X
DOI: 10.1186/gb-2014-15-1-r1
OpenAlex: W1970707135
Cited by: 2,294
References: 3
FWCI: 16.31
Type: journal article
Language: en
Open access: gold
Licence: creativecommons.org/licenses/by/2.0/ (tdm)
creativecommons.org/licenses/by/2.0/ (vor)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.

Dynamic regulation of genome-wide pre-mRNA splicing and stress tolerance by the Sm-like protein LSm5 in Arabidopsis

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Metadata

Record

About these checks

Data sources