UCLA

Ectopically expressing the transcription factors (TFs) Oct4, Sox2, Klf4, and c-Myc (OSKM) leads to the reprogramming of somatic cells to induced pluripotent stem cells (iPSCs). iPSC reprogramming takes several weeks and yields pluripotent cells only at low frequencies indicating that the reprogramming factors need to overcome barriers established in somatic cells to preserve cell identity. Yet, the mechanisms driving the successful decommissioning of the starting somatic program and the activation of the target pluripotency program are currently unclear. A recent study from the Plath lab has begun to determine how OSKM induce the remodeling of enhancers and induce thetransition from somatic to pluripotency enhancers during the reprogramming of mouse embryonic fibroblasts (MEFs) to iPSCs. The key finding was that the collaborative binding of OSK is essential for the step-wise selection and activation of pluripotency enhancers (PEs) throughout reprogramming. Consequently, the target sites of OSKM gradually change during reprogramming to mediate the step-wise induction of pluripotency enhancers. Intriguingly, the reprogramming factors also act on MEF enhancers (MEs). Based on ChIP-seq results, it was suggested that OSK redirect somatic (endogenously expressed) TFs away from MEs to new sites opened by the reprogramming factors. Concomitantly, the active enhancer mark H3K27ac is decreasing at MEs, suggesting that the movement of somatic TFs is critical for the inactivation of MEs. The key questions in the field now are to understand how OSKM, at a mechanistic level, induce (i) the redistribution of somatic TFs and (ii) the decommissioning of MEs in the early stage of reprogramming.In my graduate work, I am addressing these questions by taking advantage of co- mentorship in the Plath and Wohlschlegel labs. In Chapter 2, I hypothesize that both protein-protein interactions (PPIs) of the reprogramming factors with somatic TFs and the newly opened sites containing somatic TFs’ binding motifs are critical for the redistribution of somatic TFs binding and the following somatic enhancer decommissioning. Consequently, I am combining functional experiments with mass spectrometry (MS) methodologies to i) define co-binding between OSK and somatic TFs, ii) to distinguish mechanisms of somatic TFs redistribution through direct PPIs, cooperative binding, and open-sites free binding and iii) identify the mechanism of how active histone mark is removed from MEs. However, since TFs are often of low abundance in cells, MS approaches with a large dynamic range are required for the identification and quantification. Therefore, another aspect of my graduate work is to develop and optimize cutting-edge bottom-up proteomics methodologies for the assessment of low abundance proteins. In Chapter 3, to achieve the better identification and quantification of lowly abundant proteins in complex protein mixtures, I developed a bead-based off-line peptide fractionation method termed: CMMB (Carboxylate Modified Magnetic Bead) -based isopropanol gradient peptide fractionation or ‘CIF’. CIF provides an effective but low material loss alternative to other fractionation methods. In Chapter 4, by combining optimized proteomics and cell biology approaches, we uncovered an understudied mechanism of nuclear proteome regulation: activity- dependent proteasome-mediated degradation. We found that the tumor suppressor protein PDCD4 undergoes rapid stimulus-induced degradation in the nucleus of neurons. We demonstrate that degradation of PDCD4 is required for normal activity-dependent transcription and that PDCD4 target genes include those encoding proteins critical for synapse formation, remodeling, and transmission. In Chapter 5, for improving the quantification of proteins of interest, targeted proteomics assays are often used to pursue more accurate quantitation and better sensitivity. The recently launched High-field asymmetric waveform ion mobility spectrometry (FAIMS) device enables the possibility of improving conventional targeted proteomics assay data quality, while such improvement relies heavily on tuning the parameters of the FAIMS settings, in my thesis work, I investigated the molecular determinants underlying peptide separation by FAIMS and demonstrate that the machine learning model can be used to predict optimized FAIMS settings for peptides which significantly improves targeted proteomics workflows.