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UC Berkeley UC Berkeley Electronic Theses and Dissertations UC Berkeley UC Berkeley Electronic Theses and Dissertations Title Networks of Splice Factor Regulation by Unproductive Splicing Coupled With NMD Permalink https://escholarship.org/uc/item/4md923q7 Author Desai, Anna Publication Date 2017 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California Networks of Splice Factor Regulation by Unproductive Splicing Coupled With NMD by Anna Maria Desai A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Comparative Biochemistry in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Steven E. Brenner, Chair Professor Donald Rio Professor Lin He Fall 2017 Abstract Networks of Splice Factor Regulation by Unproductive Splicing Coupled With NMD by Anna Maria Desai Doctor of Philosophy in Comparative Biochemistry University of California, Berkeley Professor Steven E. Brenner, Chair Virtually all multi-exon genes undergo alternative splicing (AS) to generate multiple protein isoforms. Alternative splicing is regulated by splicing factors, such as the serine/arginine rich (SR) protein family and the heterogeneous nuclear ribonucleoproteins (hnRNPs). Splicing factors are essential and highly conserved. It has been shown that splicing factors modulate alternative splicing of their own transcripts and of transcripts encoding other splicing factors. However, the extent of this alternative splicing regulation has not yet been determined. I hypothesize that the splicing factor network extends to many SR and hnRNP proteins, and is regulated by alternative splicing coupled to the nonsense mediated mRNA decay (NMD) surveillance pathway. The NMD pathway has a role in preventing accumulation of erroneous transcripts with dominant negative phenotypes. During the pioneer round of translation, NMD recognizes mRNA transcripts with in-frame premature termination codons (PTCs) and degrades them. Generally, NMD is thought to play a protective role by degrading transcripts that may generate truncated proteins that can be non-functional or deleterious. The NMD pathway also has physiological targets: it impacts gene expression through alternative splicing coupled with NMD. In this mode of regulation, high levels of one splicing factor cause target pre-mRNAs to be spliced into unproductive isoforms and degraded, resulting in lower levels of the spliced RNAs. Interestingly, many splicing factors undergo this mode of regulation. For example, SR proteins SRSF1, SRSR2, SRSF3, and SRSF7 are known to auto-regulate their own expression by coupling alternative splicing and NMD. In addition, splice factors hnRNP L and PTB are regulated in the same manner. Evidence also exists that splicing factors cross regulate each other via NMD. Since all 12 canonical human SR factors and many hnRNP factors have at least one isoform that contains evolutionarily conserved in-frame PTC, it is possible that this mode of gene regulation extends to all SR splicing factors, many hnRNP factors, and even beyond, forming a regulatory network that is dependent upon NMD. Approximately 18% of expressed genes are reported to be natural targets of NMD, yet it still remains unclear why the human genome would express mRNAs that are immediately degraded by the NMD pathway. It is especially intriguing that splicing 1 factors, which are responsible for the entire proteomic diversity, are enriched in this pool of natural NMD targets. To date, there has been no comprehensive and systematic study of human splicing factors and their role in genome wide gene regulation via NMD. Regulation via alternative splicing coupled to NMD requires binding of a splicing factor to the regulated mRNA. CLIP-seq and related studies reveal that splicing factors bind abundantly to all transcripts of our selected 100 splicing factors. In collaboration with Arun Desai, I characterized the network of protein-RNA interactions between splicing factors. I find that splicing factors form a highly-connected network, where 30-60% of all possible interactions between splicing factors and the transcripts encoding splicing factors are observed. Dr. Zhiqiang Hu and I compared the hierarchy of splicing factors to the hierarchy of transcription factors. Dr. Hu calculated hierarchies of transcription and splicing factors using ENCODE ChIP-seq and eCLIP data, applying a hierarchy metric described in Gerstein et al. (Nature 2012 489:91-100). Our limited data show that the hierarchy among splicing regulators is different from that of transcription factors. Gerstein et al. plot networks in 3 layers, with a top “executive” layer, the bottom under- regulation layer, and a middle layer in between. Unlike transcription factors which concentrate at the extremes of hierarchy metric, splicing factors form a hierarchical network that has nearly uniform distribution of proteins across the hierarchy metric and thus less clearly defined separation into the three distinct layers. Nearly all splicing factors that bind their own transcripts are found in the middle layer. Dr. Courtney French, Dr. Hu, and I combined experimental data and a model for NMD mechanism to identify targets of NMD. I inhibited NMD in HeLa and GM12878 cells via knockdown of UPF1 and SMG6, two core NMD factors, and by exposure to cycloheximide (CHX). Dr. French and Dr. Hu performed RNA-seq data analysis for targets of NMD. We observed that NMD factor knockdown is likely a better method to identify NMD targets than the CHX treatment. We found that approximately 30% of NMD isoforms are shared between HeLa and GM12878, while the remainder are not substantially expressed in the other cell line. 2 CHAPTER 1 NETWORK OF SPLICE FACTOR REGULATION BY UNPRODUCTIVE SPLICING .......................... 1 ABSTRACT ................................................................................................................................................... 1 INTRODUCTION ............................................................................................................................................ 1 RESULTS ..................................................................................................................................................... 6 DISCUSSION ............................................................................................................................................... 19 MATERIALS AND METHODS .......................................................................................................................... 21 CHAPTER 2 TRANSCRIPTOME-WIDE IDENTIFICATION OF POTENTIAL RUST TARGETS REVEALS EXTENSIVE REDUNDANCY BETWEEN HELA AND GM12878 .................................................................. 24 ABSTRACT ................................................................................................................................................. 24 INTRODUCTION .......................................................................................................................................... 24 RESULTS ................................................................................................................................................... 26 DISCUSSION ............................................................................................................................................... 54 MATERIALS AND METHODS .......................................................................................................................... 56 CHAPTER 3 REFERENCES ..................................................................................................................... 63 i List of Figures Figure 1.1. Experimentally proven RUST network. .......................................................... 7 Figure 1.2. Splicing factor-mRNA interaction network. ................................................... 10 Figure 1.3 Splicing factor-mRNA interaction network extended to 100 splicing regulators ................................................................................................................................ 14 Figure 1.4 Comparison of hierarchies of TF-TF network and SF-SF network in K562 cell line ........................................................................................................................... 16 Figure 1.5. Hierarchies of TF-TF network in GM12878 cell line and SF-SF network in Hep2 cell line. .......................................................................................................... 17 Figure 1.6. All evaluated splicing factors bind transcripts of other splicing factors more prevalently than transcripts of other genes. ............................................................ 18 Figure 2.1 Experimental validation of SMG6 and UPF1 knockdown in HeLa and GM12878 cells by qPCR and western blots. ........................................................... 28 Figure 2.2. Validation of NMD inhibition through SRSF6 isoform expression alterations. ................................................................................................................................ 29 Figure 2.3. Impact of SMG6/UPF1 knock down and CHX treatment on PTC50 and non- PTC50 isoforms....................................................................................................... 31 Figure 2.4. Differentially expressed PTC50 and non-PTC50 isoforms upon NMD inhibition in HeLa and GM12878 cells. .................................................................... 34
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