Dynamic changes in intron retention are tightly associated with regulation of splicing factors and proliferative activity during B-cell development - PubMed
- ️Wed Jan 01 2020
Dynamic changes in intron retention are tightly associated with regulation of splicing factors and proliferative activity during B-cell development
Sebastian Ullrich et al. Nucleic Acids Res. 2020.
Abstract
Intron retention (IR) has been proposed to modulate the delay between transcription and translation. Here, we provide an exhaustive characterization of IR in differentiated white blood cells from both the myeloid and lymphoid lineage where we observed highest levels of IR in monocytes and B-cells, in addition to previously reported granulocytes. During B-cell differentiation, we found an increase in IR from the bone marrow precursors to cells residing in secondary lymphoid organs. B-cells that undergo affinity maturation to become antibody producing plasma cells steadily decrease retention. In general, we found an inverse relationship between global IR levels and both the proliferative state of cells, and the global levels of expression of splicing factors. IR dynamics during B-cell differentiation appear to be conserved between human and mouse, suggesting that IR plays an important biological role, evolutionary conserved, during blood cell differentiation. By correlating the expression of non-core splicing factors with global IR levels, and analyzing RNA binding protein knockdown and eCLIP data, we identify a few splicing factors likely playing an evolutionary conserved role in IR regulation. Our work provides new insights into the role of IR during hematopoiesis, and on the main factors involved in regulating IR.
© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.
Figures

Intron retention (IR) in differentiated primary human blood cells from the Blueprint consortium. (A) Differentiated cells of the hematopoietic tree. Fraction of genes with IR level above displayed threshold (20, 40 and 60%) for the highest retained intron per gene. Cell types identified by color code, subtypes by the X-axis labels. (cell type drawings adapted from OpenStax Anatomy and Physiology Textbook Version 8.25). (B) First two principal components of the PCA based on the IR values from the introns quantified in all samples (i.e. introns with cell type specific IR were omitted). Line indicates separation between the lymphoid (L) and myeloid (M) branch. (C) Fraction of genes with >50% retention of their most retained intron.

Intron retention during human and mouse B-cell differentiation. (A) Human B-cell differentiation and migration from blood to tonsil with release of memory cells back into blood. Fraction of genes with IR level above displayed threshold (20, 40 and 60%) for the highest retained intron per gene. Differentiation stages identified by color code. (B) Murine B-cell differentiation and migration from bone marrow to spleen. Fraction of genes with IR level above displayed threshold (20, 40 and 60%) for the highest retained intron per gene. Differentiation stages identified by color code.

Correlation of intron retention with gene expression during mouse B-cell differentiation. (A) RNASeq read distribution along the Tra2a gene in the seven stages of B-cell differentiation (dIR = 0.50 between MZ B-cells and GC B-cells). Read density along intron 7 (dashed grey box) recapitulates the patterns observed globally in Figure 2B (B) Gene-wise Spearman correlation between IR of the highest differential retained intron per gene and the expression of that gene (X-axis) versus dIR (Y-axis). The correlations are computed along 35 samples for 7786 genes. The distribution of the correlation is shown at the top of the plot. The horizontal dashed line identifies the top 100 genes with the highest dIR. Genes involved in cell cycle, splicing and NF-κB are highlighted. (C) Spearman correlation between the expression of the proliferation marker Mki67 and IR along the murine B-cell differentiation samples. Each dot represents Mki67 expression and the fraction of genes that have at least one intron retained with an IR value >0.4 for a given sample.

Differential IR during murine B-cell differentiation and splicing factors (A) Splice site strength of introns with high differential retention (dIR>0.2) during murine B-cell differentiation compared with size matched introns with low differential retention (dIR < 0.02). Both 5′ (P-value 1.08e−09, both-sided t-test) and 3′ splice sites (P-value 9.98e−08, both-sided t-test) had significantly lower splice site strength (maximum entropy) in introns with high dIR. (B) RNASeq read distribution and conservation along the Fus transcript. Introns 6 and 7 (dashed grey box) with the highest differential retention also have the highest conservation among vertebrates. (C) Distribution of the expression of 126 murine splicing related genes, including 95 core spliceosomal factors compared to protein coding genes (n = 21 837) along murine B-cell differentiation. Each differentiation stage contains values of five biological replicates. (D) Expression of 31 non-core splicing factors (SR-rich and hnRNP) during murine B-cell differentiation. Genewise z-scores of log10 transformed FPKM values are used. (E) Spearman correlation of the SR-rich and hnRNP gene expression values with the overall fraction of genes that have at least one intron retained per gene with an IR value > 0.2 in the given sample. Splicing factors overlapping with those in panel F) highlighted in red. (F) Enriched RNA Binding Protein (RBP) binding sites (from a set of 102 RBP motifs) in introns with dIR>0.2 between murine MZ and GC B-cells in comparison with size matched introns with low dIR. Splicing factors overlapping within those in panel E) are highlighted in red.

Candidate splicing factors to regulate IR (data from the human ENCODE project). (A) Number of introns that increase retention (dIR > 0.2) upon shRNA knockdown of 58 splicing factors in K562 cells from ENCODE. Mean values of two technical replicates per splicing factor normalized by the median of non-targeted control samples. Core splicing factors in grey, non-core splicing factors in black and non-core factors with enriched binding motives in dIR introns 4F) in red. (B) Hierarchical clustering based on IR of the 3102 retained introns with dIR >0.2 for at least one splicing factor knockdown. Mean values of two technical replicates per splicing factor normalized by the median of non-targeted control samples and z-transformed by intron. While core splicing factors like U2AF1 and U2AF2 affect many introns, SR-rich and hnRNP proteins like SRSF1 and HNRNPK (boxed in the plot) have rather distinct sets of intronic targets (identified by arrows). (C) eCLIP derived HNRNPK and SRSF1 strength of binding in the top 100 introns each with their strongest effect on dIR upon their knockdown compared to the strength of binding of nine other non-core splicing factors (see methods) within the same introns in K562 cells. Values are the maximum signal (see Materials and Methods) values within the intronic sequence. (D) RNASeq data of the HNRNPK (blue) and SRSF1 (black) knockdown experiments versus an untreated control (red) in K562 cells from ENCODE for the gene ARFIP2 (top rows). eCLIP binding data for HNRNPK (light blue) and SRSF1 (grey) along the sequence of ARFIP2 (bottom row). Most affected introns are marked by a dashed grey box.

General model of changes in IR, splicing factor expression and proliferation during B-cell differentiation.
Similar articles
-
Ge Y, Porse BT. Ge Y, et al. Bioessays. 2014 Mar;36(3):236-43. doi: 10.1002/bies.201300156. Epub 2013 Dec 18. Bioessays. 2014. PMID: 24352796 Review.
-
Parra M, Booth BW, Weiszmann R, Yee B, Yeo GW, Brown JB, Celniker SE, Conboy JG. Parra M, et al. RNA. 2018 Sep;24(9):1255-1265. doi: 10.1261/rna.066951.118. Epub 2018 Jun 29. RNA. 2018. PMID: 29959282 Free PMC article.
-
Orchestrated intron retention regulates normal granulocyte differentiation.
Wong JJ, Ritchie W, Ebner OA, Selbach M, Wong JW, Huang Y, Gao D, Pinello N, Gonzalez M, Baidya K, Thoeng A, Khoo TL, Bailey CG, Holst J, Rasko JE. Wong JJ, et al. Cell. 2013 Aug 1;154(3):583-95. doi: 10.1016/j.cell.2013.06.052. Cell. 2013. PMID: 23911323
-
Wong JJ, Au AY, Ritchie W, Rasko JE. Wong JJ, et al. Bioessays. 2016 Jan;38(1):41-9. doi: 10.1002/bies.201500117. Epub 2015 Nov 27. Bioessays. 2016. PMID: 26612485 Review.
-
Pimentel H, Parra M, Gee SL, Mohandas N, Pachter L, Conboy JG. Pimentel H, et al. Nucleic Acids Res. 2016 Jan 29;44(2):838-51. doi: 10.1093/nar/gkv1168. Epub 2015 Nov 3. Nucleic Acids Res. 2016. PMID: 26531823 Free PMC article.
Cited by
-
Hackert NS, Radtke FA, Exner T, Lorenz HM, Müller-Tidow C, Nigrovic PA, Wabnitz G, Grieshaber-Bouyer R. Hackert NS, et al. Nat Commun. 2023 Dec 8;14(1):8133. doi: 10.1038/s41467-023-43573-9. Nat Commun. 2023. PMID: 38065997 Free PMC article.
-
Chen SX, Simpson E, Reiter JL, Liu Y. Chen SX, et al. Hum Mutat. 2022 Nov;43(11):1629-1641. doi: 10.1002/humu.24379. Epub 2022 May 10. Hum Mutat. 2022. PMID: 35391504 Free PMC article.
-
Kubota H, Ueno H, Tasaka K, Isobe T, Saida S, Kato I, Umeda K, Hiwatari M, Hasegawa D, Imamura T, Kakiuchi N, Nannya Y, Ogawa S, Hiramatsu H, Takita J. Kubota H, et al. Blood Adv. 2024 Mar 12;8(5):1258-1271. doi: 10.1182/bloodadvances.2023011583. Blood Adv. 2024. PMID: 38127276 Free PMC article.
-
Noncoding rules of survival: epigenetic regulation of normal and malignant hematopoiesis.
Wallace L, Obeng EA. Wallace L, et al. Front Mol Biosci. 2023 Oct 31;10:1273046. doi: 10.3389/fmolb.2023.1273046. eCollection 2023. Front Mol Biosci. 2023. PMID: 38028538 Free PMC article. Review.
-
Bo S, Sun Q, Li Z, Aodun G, Ji Y, Wei L, Wang C, Lu Z, Zhang Q, Zhao X. Bo S, et al. Front Genet. 2023 Apr 12;14:1151703. doi: 10.3389/fgene.2023.1151703. eCollection 2023. Front Genet. 2023. PMID: 37124607 Free PMC article.
References
-
- DeKoter R.P., Singh H.. Regulation of B lymphocyte and macrophage development by graded expression of PU.1. Science. 2000; 288:1439–1441. - PubMed
-
- Sasaki H., Kurotaki D., Tamura T.. Regulation of basophil and mast cell development by transcription factors. Allergol. Int. 2016; 65:127–134. - PubMed
-
- Pan Q., Shai O., Lee L.J., Frey B.J., Blencowe B.J.. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet. 2008; 40:1413–1415. - PubMed
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Other Literature Sources