Peshkin L , Wühr M , Pearl E , Haas W , Freeman RM , Gerhart JC , Klein AM , Horb M , Gygi SP , Kirschner MW .

R01 GM103785 NIGMS NIH HHS , R01 HD073104 NICHD NIH HHS , P40OD010997 NIH HHS , R01GM103785 NIGMS NIH HHS , R01HD073104 NICHD NIH HHS , R01 DK077197 NIDDK NIH HHS , P40 OD010997 NIH HHS

GSE73904: Xenbase,  NCBI
GSE73905: NCBI

	Figure 1. Early Embryonic Stages in X. laevis(A) mRNA and protein were collected from various stages of development.(B) The dataset combines temporal profiles of 27,877 mRNA and 6,509 proteins and egg concentration data for 9,728 proteins.(C) A histogram of ∼8,000 cosine distances between published and new mRNA profiles. Three sample mRNA profiles—Chordin, Tenascin N, and Secernin—are given as published (solid) and new RNA-seq data (dashed).(D) A histogram of 35 cosine distances between published and new protein abundance changes. Three proteins quantified via western blot (solid) and multiplexed proteomics (dashed) with representative cosine distances color coded.
	Figure 2. Allo-Alleles Are Concordant in Both Protein and mRNA Expression(A) Peptides with a single amino acid difference (red) used to distinguish the allo-alleles.(B) mRNA and protein expression in allo-alleles of DAPL1.(C) Histogram of cosine distance over temporal expression in 164 allo-allele pairs of proteins (right) and 630 pairs of mRNA (left). Median cosine distances are 0.006 and 0.04, respectively. Median Pearson correlations are 0.94 and 0.85, respectively. The cosine distance between protein and mRNA pair of DAPL1 profiles is 0.004 and 0.03, respectively, exemplifying the median discordance as shown by colored triangle positions. Gray histograms show the baseline distribution obtained by randomly re-matching allo-alleles.(D) Scatter plot of cumulative protein concentration for allo-alleles. The overall rank correlation between allo-alleles is 0.50.
	Figure 3. Most Proteins Change Little in Level from Egg through Tailbud Stages(A) K-means clustering of relative protein abundance into nine clusters using cosine distance, labeled by the number of proteins that fall into each cluster represented by the median curve. The thickness of the median line reflects the number of proteins in the cluster.(B) Histogram of protein dynamicity shows that most proteins do not change much within the surveyed period. The insert shows representative examples: (gray dashed line) TPI1 (Triosephosphate Isomerase 1) is among the flattest possible with δ = 2.0e-04. RPL11 (black) is at the median of the dynamicity distribution (δ = 0.8e-2). OCM2 (a calmodulin) and one of the isoforms of hemoglobin zeta (HBZ), a form of alpha globin produced in the yolk sack of mammals, are among the most dynamic (δ = 0.571; 35 degrees difference) proteins. Color code: red for dynamic, black for flat.(C) Highly abundant proteins are generally flat, while low abundance proteins are mostly dynamic. The density plot of absolute protein concentration in the egg against dynamicity is shown.
	Figure 4. Temporal Expression of Tissue-Specific Proteins(A) Histogram of tissue specificity over all measured proteins with the lowest and the highest 25% quantiles color coded. Sample nonspecific genes are elongation factors and proteasome, while specific are myosin and creatine kinase.(B) Fraction of “non-specific/specific” proteins found in the two most representative clusters.
	Figure 5. Discordance of Temporal Patterns in mRNA and Protein Expression(A) Rank correlation (Spearman) within developmental stage between protein and mRNA temporal patterns for ribo-depleted and poly(A)-enriched methods of mRNA measurement.(B) Histogram of Pearson correlation between protein and mRNA temporal change patterns.(C) Exemplary mRNA-protein time series. The ordinates represent the relative concentration of protein to mRNA. Each plot shows the estimated absolute concentration of mRNA and protein.(D) Mutual information between the temporal pattern of expression for mRNA and protein presented as co-clustering into three key trends. The grayscale background reflects the number of genes in each cluster. The left column illustrates that a flat protein pattern may correspond to any mRNA pattern, but if the protein is dynamic, it usually follows respective change in the mRNA concentration (see top of the right column for induction). Criss-cross patterns of anti-correlation are rarely observed (bottom of the right column).
	Figure 6. Mass Action Kinetics Equation Results in a Plausible Model of Embryonic Protein Economy(A) Robust fitting of solution to the equation dp/dt = KSr(t) − KDp(t) is done by searching a combination of synthesis and degradation rates minimizing the mean square difference in protein level (see equation above the plot). The beige stripe shows the 95% confidence band for protein dynamics, which corresponds to the 95% confidence range in synthesis and degradation rates. This region includes actual protein measurements marked via green discs. The no-degradation model is selected.(B) Venn diagram of models of different complexity.(C) Histograms of half-life (right) and synthesis rate (left). Half-life is given in hours, while synthesis rate is given in moles of protein synthesized per mole of mRNA per hour. The green triangles indicate the medians.(D) Histogram of Pearson correlation for model-based versus measured protein expression for a model assuming median synthesis and median degradation rates while using the actual initial concentration.
	Figure 7. Embryonic Protein EconomyEmbryonic protein economy expressed as gradual replacement of maternal by zygotic protein, integrated over all proteins fitted by our model and extrapolated to the whole embryo.

Ben-Tabou de-Leon, Modeling the dynamics of transcriptional gene regulatory networks for animal development. 2009, Pubmed

Ben-Tabou de-Leon, Modeling the dynamics of transcriptional gene regulatory networks for animal development. 2009, Pubmed
Bowes, Xenbase: gene expression and improved integration. 2010, Pubmed , Xenbase
Dean, Pervasive and persistent redundancy among duplicated genes in yeast. 2008, Pubmed
Force, Preservation of duplicate genes by complementary, degenerative mutations. 1999, Pubmed
Gall, Formation and detection of RNA-DNA hybrid molecules in cytological preparations. 1969, Pubmed , Xenbase
Gujral, Exploiting polypharmacology for drug target deconvolution. 2014, Pubmed , Xenbase
Gurdon, The use of Xenopus oocytes for the expression of cloned genes. 1983, Pubmed , Xenbase
Heasman, Beta-catenin signaling activity dissected in the early Xenopus embryo: a novel antisense approach. 2000, Pubmed , Xenbase
Hogan, Diverse RNA-binding proteins interact with functionally related sets of RNAs, suggesting an extensive regulatory system. 2008, Pubmed
Howe, Identification of a developmental timer regulating the stability of embryonic cyclin A and a new somatic A-type cyclin at gastrulation. 1995, Pubmed , Xenbase
Jorgensen, The mechanism and pattern of yolk consumption provide insight into embryonic nutrition in Xenopus. 2009, Pubmed , Xenbase
Junker, Genome-wide RNA Tomography in the zebrafish embryo. 2014, Pubmed
Langmead, Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. 2009, Pubmed
Lee, Temporal and spatial regulation of fibronectin in early Xenopus development. 1984, Pubmed , Xenbase
Li, RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. 2011, Pubmed
McAlister, MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. 2014, Pubmed
McGivern, Toward defining the phosphoproteome of Xenopus laevis embryos. 2009, Pubmed , Xenbase
Newport, A major developmental transition in early Xenopus embryos: I. characterization and timing of cellular changes at the midblastula stage. 1982, Pubmed , Xenbase
Peter, Predictive computation of genomic logic processing functions in embryonic development. 2012, Pubmed
Schwanhäusser, Global quantification of mammalian gene expression control. 2011, Pubmed
Smits, Global absolute quantification reveals tight regulation of protein expression in single Xenopus eggs. 2014, Pubmed , Xenbase
Stasevich, Regulation of RNA polymerase II activation by histone acetylation in single living cells. 2014, Pubmed
Struhl, A gene product required for correct initiation of segmental determination in Drosophila. 1981, Pubmed
Sun, Quantitative proteomics of Xenopus laevis embryos: expression kinetics of nearly 4000 proteins during early development. 2014, Pubmed , Xenbase
Vastag, Remodeling of the metabolome during early frog development. 2011, Pubmed , Xenbase
Vizcaíno, ProteomeXchange provides globally coordinated proteomics data submission and dissemination. 2014, Pubmed
Vogel, Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. 2012, Pubmed
Wessel, A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids. 1984, Pubmed
Wühr, The Nuclear Proteome of a Vertebrate. 2015, Pubmed , Xenbase
Wühr, Accurate multiplexed proteomics at the MS2 level using the complement reporter ion cluster. 2012, Pubmed , Xenbase
Wühr, Deep proteomics of the Xenopus laevis egg using an mRNA-derived reference database. 2014, Pubmed , Xenbase
Yanai, Mapping gene expression in two Xenopus species: evolutionary constraints and developmental flexibility. 2011, Pubmed , Xenbase
Zhao, Regulation of cellular metabolism by protein lysine acetylation. 2010, Pubmed