シグナル伝達から遺伝子発現にかけて異なる時間スケールで変動する生命現象のシステム同定

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 飯野 雄一, 東京大学教授 黒田 真也, 東京大学准教授 津村 幸治, 東京大学准教授 程 久美子, 工学院大学准教授 小西 克也University of Tokyo(東京大学

Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling.

Cellular signaling processes can exhibit pronounced cell-to-cell variability in genetically identical cells. This affects how individual cells respond differentially to the same environmental stimulus. However, the origins of cell-to-cell variability in cellular signaling systems remain poorly understood. Here, we measure the dynamics of phosphorylated MEK and ERK across cell populations and quantify the levels of population heterogeneity over time using high-throughput image cytometry. We use a statistical modeling framework to show that extrinsic noise, particularly that from upstream MEK, is the dominant factor causing cell-to-cell variability in ERK phosphorylation, rather than stochasticity in the phosphorylation/dephosphorylation of ERK. We furthermore show that without extrinsic noise in the core module, variable (including noisy) signals would be faithfully reproduced downstream, but the within-module extrinsic variability distorts these signals and leads to a drastic reduction in the mutual information between incoming signal and ERK activity

Additional file 1 of Transcription factor-binding k-mer analysis clarifies the cell type dependency of binding specificities and cis-regulatory SNPs in humans

Additional file 1: Figure S1 Filtering of ChIP-seq samples. A: Schematic overview of ChIP-seq sample filtering. B: Violin plot showing the AUROC of the prediction of the top 10% PWM-supported k-mers based on the MOCCS2score. The red violin plot represents all CTCF ChIP-seq samples, the green plot represents soft-filtered CTCF ChIP-seq samples, and the blue plot represents hard-filtered CTCF ChIP-seq samples. High-quality ChIP-seq samples with high AUROC scores were retained after hard filtering. C: Distribution of each quality control metric of ChIP-seq sample filtering for samples that passed the hard filter (pink) and others (blue). D: Bar plots display the number of ChIP-seq samples that passed through the soft and hard filters. Bars are colored according to cell type classes or TFs. Figure S2 Simulation of significant k-mer detection. A: The procedure for generating simulated datasets. Simulated data generated by embedding a “true significant k-mer” within random sequences was applied to MOCCS2 and the q-values of the MOCCS2score were calculated for each k-mer. B: Parameters for each simulation condition from #1 to #5. α is the percentage of input sequences containing embedded “true significant k-mers” , N is the number of peaks in a ChIP-seq sample, and σ is the standard deviation of the embedded “true significant k-mers” from the center of the peak. C: Simulation results for significant k-mer detection. The sensitivity, specificity, and FDR for detecting “true significant k-mers” are shown for different parameter settings. Figure S3 Number of peaks and significant k-mers in MOCCS profiles. A: Number of peaks in MOCCS profiles. The x-axis represents the log-transformed number of peaks with a base of 10 and the y-axis represents the number of ChIP-seq samples. B: Relationship between the number of peaks and significant k-mers in MOCCS profiles (left, q < 0.05; right, q < 0.01). Figure S4 Similarities in MOCCS profiles and peak locations for sample pairs of same or different TFs. A: Comparison of k-sim Jaccard, Pearson and peak overlap indices (a-c: groups of the same cell types). B: Two-dimensional density plot of k-sim Jaccard or Pearson with the peak overlap index (a-c: groups of the same cell types). C: Correlation coefficient of k-sim Jaccard or Pearson with the peak overlap index in each group. The y-axis indicates Spearman’ s correlation coefficient. Red and blue indicate k-sim Pearson and Jaccard values, respectively (a-c: groups of the same cell types) Figure S5 Similarities in MOCCS profiles and peak locations for sample pairs of same/different cell types. A: Comparison of the k-sim Jaccard, Pearson, and peak overlap indices (a, d, and e: groups of the same TFs). B: Two-dimensional density plot of k-sim Jaccard or Pearson with the peak overlap index (a, d, and e: groups of the same TFs). C: Correlation coefficient of k-sim Jaccard or Pearson with the peak overlap index in each group. The y-axis indicates Spearman’ s correlation coefficient. Red and blue indicate k-sim Pearson and Jaccard values, respectively (a, d, and e: groups of the same TFs). Figure S6 Heat maps of cell type-dependent TFs. The heat map color indicates the k-sim Jaccard value for the 33 cell type-dependent TFs. The color labels of the heat maps indicate the cell type classes. Cell type classes with only a single ChIP-seq sample were excluded from the visualization. Asterisks indicate the statistical significance of ChIP-seq samples with the same and different cell type classes (Mann–Whitney U test, p < 0.05). Figure S7 Violin plots of all cell type-dependent TFs. The y-axis indicates the k-sim Jaccard value. The same and different groups were arranged along the x-axis. Asterisks indicate the statistical significance of ChIP-seq samples with the same and different cell type classes (Mann–Whitney U test, p < 0.05). Figure S8 Simulation of differential k-mer detection. A: Simulated data processing. Simulated data with an embedded “true differential k-mer” and “true significant k-mer” was prepared by embedding a “true” k-mer within α% of a randomly generated sample of 2W + 1 bp (W = 350) DNA sequences and applied to MOCCS2. “True significant k-mers” were embedded following a normal distribution whose mean was W + 1 and whose standard deviation was σ. “True differential k-mers” were embedded in S1 (or S2), similar to “true significant k-mers,” and were embedded in S2 (or S1) following a uniform distribution whose mean was 1 and whose standard deviation was (2 × W + 1) − (k − 1). It should be noted that we set k as k=6. B: Parameters for each simulation condition from #1 to #5. L is the number of differential k-mers and m is the number of significant k-mers. Figure S9 ΔMOCCS2score profiles were consistent with the in vitro SNP-SELEX and PWM motif fold change. A: Spearman’ s correlation coefficient between PBS (SNP-SELEX) and ΔMOCCS2score in each TF for the original and permuted data. Red points indicate the original Spearman’ s correlation coefficient, and blue points indicate the permutated data. B: Difference in ΔMOCCS2score profile consistency among the positions of SNPs in k-mers. The kth SNP position indicates the kth allele on the left side of the k-mer. C: The ΔMOCCS2score is consistent with the PWM motif fold change. Figure S10 Number of peak-overlapping GWAS-SNPs with significant ΔMOCCS2scores. Number of peak-overlapping GWAS-SNPs in each ChIP-seq sample. Each bar represents a ChIP-seq sample, and the y-axis represents the number of peak-overlapping GWAS-SNPs. The red fraction represents the number of peak-overlapping GWAS-SNPs with significant ΔMOCCS2scores (q < 0.05), and the gray fraction represents the number of GWAS SNPs with non-significant ΔMOCCS2scores. Figure S11 Prediction of SNP-affected TFs and cell type classes using ΔMOCCS2score profiles. Top ChIP-seq samples with high ΔMOCCS2scores in each phenotype (IBD, inflammatory bowel disease; CD, Crohn’ s disease; MS, multiple sclerosis; SLE, systemic lupus erythematosus). The ΔMOCCS2score was calculated for each SNP and ChIP-seq sample. Bar graph colors represent TFs or cell type classes. Figure S12 Association between the allele frequency and ΔMOCCS2score. Association between the allele frequency and (A) the absolute values of the ΔMOCCS2score or (B) the ratio of SNPs with significant ΔMOCCS2scores in each phenotype (IBD, inflammatory bowel disease; CD, Crohn’ s disease; MS, multiple sclerosis; SLE, systemic lupus erythematosus). Figure S13 Accuracy of detecting canonical motifs using MOCCS2score for different k. AUROC for detecting canonical PWM motifs using the MOCCS2score in the difference of value k. The x-axis represents the ratio of PWM-supported k-mers in all k-mers and the y-axis represents the AUROC. The colors of the violin plots represent the different k values

System identification of signaling dependent gene expression with different time-scale data.

Cells decode information of signaling activation at a scale of tens of minutes by downstream gene expression with a scale of hours to days, leading to cell fate decisions such as cell differentiation. However, no system identification method with such different time scales exists. Here we used compressed sensing technology and developed a system identification method using data of different time scales by recovering signals of missing time points. We measured phosphorylation of ERK and CREB, immediate early gene expression products, and mRNAs of decoder genes for neurite elongation in PC12 cell differentiation and performed system identification, revealing the input-output relationships between signaling and gene expression with sensitivity such as graded or switch-like response and with time delay and gain, representing signal transfer efficiency. We predicted and validated the identified system using pharmacological perturbation. Thus, we provide a versatile method for system identification using data with different time scales

Laguerre Filter Analysis with Partial Least Square Regression Reveals a Priming Effect of ERK and CREB on c-FOS Induction.

Signaling networks are made up of limited numbers of molecules and yet can code information that controls different cellular states through temporal patterns and a combination of signaling molecules. In this study, we used a data-driven modeling approach, the Laguerre filter with partial least square regression, to describe how temporal and combinatorial patterns of signaling molecules are decoded by their downstream targets. The Laguerre filter is a time series model used to represent a nonlinear system based on Volterra series expansion. Furthermore, with this approach, each component of the Volterra series expansion is expanded by Laguerre basis functions. We combined two approaches, application of a Laguerre filter and partial least squares (PLS) regression, and applied the combined approach to analysis of a signal transduction network. We applied the Laguerre filter with PLS regression to identify input and output (IO) relationships between MAP kinases and the products of immediate early genes (IEGs). We found that Laguerre filter with PLS regression performs better than Laguerre filter with ordinary regression for the reproduction of a time series of IEGs. Analysis of the nonlinear characteristics extracted using the Laguerre filter revealed a priming effect of ERK and CREB on c-FOS induction. Specifically, we found that the effects of a first pulse of ERK enhance the subsequent effects on c-FOS induction of treatment with a second pulse of ERK, a finding consistent with prior molecular biological knowledge. The variable importance of projections and output loadings in PLS regression predicted the upstream dependency of each IEG. Thus, a Laguerre filter with partial least square regression approach appears to be a powerful method to find the processing mechanism of temporal patterns and combination of signaling molecules by their downstream gene expression

Temporal Decoding of MAP Kinase and CREB Phosphorylation by Selective Immediate Early Gene Expression

<div><p>A wide range of growth factors encode information into specific temporal patterns of MAP kinase (MAPK) and CREB phosphorylation, which are further decoded by expression of immediate early gene products (IEGs) to exert biological functions. However, the IEG decoding system remain unknown. We built a data-driven based on time courses of MAPK and CREB phosphorylation and IEG expression in response to various growth factors to identify how signal is processed. We found that IEG expression uses common decoding systems regardless of growth factors and expression of each IEG differs in upstream dependency, switch-like response, and linear temporal filters. Pulsatile ERK phosphorylation was selectively decoded by expression of EGR1 rather than c-FOS. Conjunctive NGF and PACAP stimulation was selectively decoded by synergistic JUNB expression through switch-like response to c-FOS. Thus, specific temporal patterns and combinations of MAPKs and CREB phosphorylation can be decoded by selective IEG expression via distinct temporal filters and switch-like responses. The data-driven modeling is versatile for analysis of signal processing and does not require detailed prior knowledge of pathways.</p> </div