Multi-Omic Profiling Reveals Dynamics of the Phased Progression of Pluripotency

Yang, Pengyi; Humphrey, Sean J.; Cinghu, Senthilkumar; Pathania, Rajneesh; Oldfield, Andrew; Kumar, Dhirendra; Perera, D.I.; Yang, Jean Yee Hwa; James, David E.; Mann, Matthias; Jothi, Raja

doi:10.1101/415430

Cited by 19 publications

(48 citation statements)

References 112 publications

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“…Consistent with previous multi-omics reports 32 , we found that phosphoprotein abundance is globally more variable than the corresponding mRNA and protein levels. While we acquired phosphoproteomic profiles for all HeLa CCL2 and Kyoto strains covered by the original study 26 (Fig.…”

Section: Development Of Deltasilac For Timing the Protein Endurance Isupporting

confidence: 92%

Global impact of phosphorylation on protein endurance

et al. 2020

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Post-translational modifications such as phosphorylation can have profound effects on the physicochemical and biological properties of proteins. However, high-throughput and systematic approaches have not yet been developed to assess the effects of specific modification types and sites on protein lifetime, which represents a key parameter for understanding signaling rewiring and drug development. Here we describe a proteomic method, DeltaSILAC, to quantify the impact of site-specific phosphorylation on the endurance of thousands of proteins in live cells. Being configured on the reproducible data-independent acquisition mass spectrometry (DIA-MS), the pulse labeling approach using stable isotope-labeled amino acids in cells (SILAC), together with a novel peptide-level matching strategy, this multiplexed assay revealed the global delaying effect of phosphorylation on protein turnover in growing cancer cells. Further, we identified local sequence and structural features in proximity to the phosphorylated sites that could be associated with protein endurance alterations. We found that phosphorylated sites accelerating protein turnover are functionally selected for cell fitness and evolutionarily conserved. DeltaSILAC provides a generalizable approach for prioritizing the effects of phosphorylation sites on protein lifetime in the context of cell signaling and disease biology, which is highly complementary to existing methods. Finally, DeltaSILAC is widely applicable to diverse posttranslational modification types and different cell systems.

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Section: Development Of Deltasilac For Timing the Protein Endurance Isupporting

confidence: 92%

Global impact of phosphorylation on protein endurance

et al. 2020

Preprint

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“…We also tested Minardo-Model by applying it to a multiomics data set published by Yang et al 8 , which measured responses in mouse E14Tg2a cells as they differentiated from the naive embryonic stem cell (ESC) state to primed epiblast-like cells (EpiLC) 8 . Yang et al measured phosphoproteomics changes at 12 time points (including basal) and provided 3,585 phosphosite profiles partitioned into 4 clusters using CLUE 6 .…”

Section: Multiomics Data Setmentioning

confidence: 99%

“…resulting in many inferred events clustered together at the same time interval. This limitation becomes more pronounced with technologies, such as single-cell RNA sequencing (scRNA-seq) and multiomics experiments 8 , that can result in large numbers of clusters and events. To address this limitation, we have developed Minardo-Model, an automated, reproducible method that uses statistics to infer events ordering -in many cases, achieving better temporal resolution than the experiment.…”

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confidence: 99%

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Temporal ordering of omics and multiomic events inferred from time series data

Kaur

Yang

Luu

et al. 2020

Preprint

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Temporal changes in omics events can now be routinely measured, however current analysis methods are often inadequate, especially for multiomics experiments. We report a novel analysis method that can infer event ordering at better temporal resolution than the experiment, and integrates omic events into two concise visualizations (event maps and sparklines). Testing our method gave results well-correlated with prior knowledge and indicated it streamlines analysis of time-series data. MainA range of emerging omics and multiomics techniques now provide unprecedented ability to systematically track the time course of changes in abundance, for example, in mRNAs, proteins, or posttranslational modifications (PTMs). The resulting time-series data can then be used to infer the ordering of underlying events (e.g., activation of kinases, transcription factors, or translation factors) that explain the observed changes in abundance. These ordered events provide insight into the causal flow of information underlying transitions in cellular state. These insights, in turn, have the potential to lead to fundamental advances in biology and biomedical sciences -however, realizing this potential requires improving the analysis methods used, which are often inadequate with existing data sets 1,2 , especially from multiomics experiments.When analysing data from such experiments, a common first step is to partition the (typically) tens of thousands of abundance time profiles found into tens of clusters, each with similar temporal changes that are then visualized in a single profile plot 1,2 (Fig. 1). A wide variety of clustering methods are available, ranging from widely-used, general purpose methods (e.g., fuzzy c-means; FCM 3 ) to specialized methods tailored for specific, omics experimental scenarios 4 , 5 , 6 . The specialist methods typically combine clustering with assigning the underlying events, for example, to specific kinases or transcription factors. Often, these events are further assigned to specific time intervals used in the experiment, or to approximate time windows (e.g., early, intermediate, or late response), thus defining an implicit event ordering. However, a key limitation of current approaches is seen when plotting the inferred events using temporal-based layouts 7 : there can often be many more events than time intervals in the original experiment,

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“…To directly interrogate the effect of PFN2 on ERK activation under serum+LIF conditions, we performed FGF stimulation assays in wild-type, Dgcr8 -KO, and PFN2-mutant Dgcr8 -KO ESCs at single cell resolution. To achieve this, the cells were subjected to short-term FGF4-stimulation, a physiologically relevant cue that induces MAPK/ERK signaling and ERK activation in PSCs 24–25 , and ERK activation was measured by intracellular staining and flow cytometry. As expected, ERK activation was induced in wild-type ESCs (Fig.…”

Section: Figurementioning

confidence: 99%

MicroRNA-dependent inhibition of PFN2 orchestrates ERK activation and pluripotent state transitions by regulating endocytosis

Sangokoya

Blelloch

2020

Preprint

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Profilin2 (PFN2) is a target of the embryonic stem cell (ESC) enriched miR-290 family of miRNAs and an actin/dynamin binding protein implicated in endocytosis. Here, we show that the miR-290-PFN2 pathway regulates many aspects of ESC biology. In the absence of microRNAs, PFN2 is upregulated in ESCs, with a resulting decrease in endocytosis. Reintroduction of miR-290, knockout of PFN2, or disruption of the PFN2 dynamin interacting domain in miRNA deficient cells reverses the endocytosis defect. The loss of miRNA suppression of PFN2 and associated reduction in endocytosis impairs ERK signaling, which in turn inhibits ESC cell cycle progression and differentiation from a naïve to formative state. Mutagenesis of the single canonical conserved 3’UTR miR-290 binding site of PFN2 in otherwise wild-type cells recapitulates these phenotypes. Together, these findings define an axis of post-transcriptional control, endocytosis, and signal transduction that is essential for ESC self-renewal and differentiation.

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Multi-Omic Profiling Reveals Dynamics of the Phased Progression of Pluripotency

Cited by 19 publications

References 112 publications

Global impact of phosphorylation on protein endurance

Global impact of phosphorylation on protein endurance

Temporal ordering of omics and multiomic events inferred from time series data

MicroRNA-dependent inhibition of PFN2 orchestrates ERK activation and pluripotent state transitions by regulating endocytosis

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