Differential expression and phosphorylation in Ptenm3m4 brain
In order to assess how the m3m4 mutation affects the landscape of gene and protein expression and protein phosphorylation, we performed parallel transcriptomic , proteomic, and phosphoproteomic experiments on Ptenm3m4/m3m4 hemibrains and cortices at two-weeks- (P14) and six-weeks-of-age (P40), respectively (Fig. 1a). At P14, a total of 3,207 unique proteins, with an average of 2,345 unique proteins per sample, were identified (Additional file 1: Table S1), whereas at P40, a total of 6,635 unique proteins, with an average of 5,381 unique proteins per sample, were identified (Additional file 1: Table S2). Principal component analysis (PCA) based on the LFQ/NSAF abundance values for a given protein, at both time points revealed completely separate clusters representing homozygous mutant and wildtype samples (Additional file 2: Fig. S1A, B). At P14, a total of 4,080 unique peptides were identified, 97% of which contained phosphorylated residues in the phospho-serine/threonine scan, an average of 2,239 unique phosphopeptides per sample (Additional file 1: Table S3). At P40, a total of 8,468 unique peptides were identified, 90% of which contained phosphorylated residues, with an average of 5,783 unique phosphopeptides per sample (Additional file 1: Table S4). PCA, based on the LFQ abundance values for a given phosphopeptide, at both time points revealed non-overlapping clusters of homozygous mutant and wildtype samples (Additional file 2: Fig. S1C, D). These findings illustrate robust sampling of the proteome and phosphoproteome, and the captured variability differentiated homozygous mutant and wildtype brain samples well.
In assessing differentially expressed proteins, we found 24 over-expressed and 27 under-expressed proteins in the P14 mutants compared to wildtype controls (Table 1; Fig. 1b). At P40, we identified 150 over-expressed and 102 under-expressed proteins in the mutant brains compared to the wildtype brains (Table 1; Fig. 1b). When assessing differential phosphorylation of phospho-serine/threonine phosphopeptides, we found 1 increased and 99 decreased in the P14 mutant hemibrain (Table 1; Fig. 1c). Moreover, we found 113 increased and 185 decreased at P40 in the mutant cortex (Table 1; Fig. 1c). In the special phospho-tyrosine-specific phospho-scan, we identified 39 and 29 relatively increased phosphopeptides at P14 and P40, respectively (Table 1). In the same phospho-tyrosine scan, we found 29 and 26 decreased phosphopeptides at P14 and P40, respectively (Table 1). Overall, the over-expression/under-expression of proteins or enrichment/depletion of phosphopeptides were distributed roughly equally except in the P14 phospho-serine/threonine scan, where phosphopeptide depletion was heavily favored in the mutant by 99-fold (Table 1). Moreover, there is a general increase in differential expression and phosphorylation as the Ptenm3m4/m3m4 mice age, a trend not observed in the phospho-tyrosine scan (Table 1).
Dissimilarity across –omic datasets describing the Ptenm3m4brain
Given the eight different –omic datasets spanning two time points, we sought to understand what molecules, showing differential expression/phosphorylation in the homozygous mutant brain, were shared among these datasets. Thus, we performed pairwise comparisons of the molecule lists of each separate –omics approach that compared Ptenm3m4/m3m4 to Pten+/+ mice for both P14 and P40 time points. Surprisingly, we found no intersection between the P14 and P40 proteome (Fig. 2a). For the phospho-serine/threonine scan, we found 21 molecules or roughly 7% of all input molecules shared between time points (Fig. 2b). For the phospho-tyrosine scan, we found 25 molecules or roughly 32% of all input molecules shared between time points (Fig. 2c). Furthermore, 49 molecules were shared between the P14 and P40 transcriptome datasets or roughly 4% of all input molecules (Fig. 2d). These analyses indicate little to marginal overlap within the various –omic scans over developmental time.
Additionally, we assessed overlap among the proteomic and phosphoproteomic datasets within their respective time points. Surprisingly, we found almost no intersection across these datasets (Fig. 2e). We proceeded to assess the overlap between the transcriptome and proteome within each time point and found 1 and 30 intersecting molecules at P14 and P40, respectively (Fig. 2f). Although we expected to find more shared genes between the transcriptome and proteome, these observations suggest that the mechanisms governing changes in protein expression are separate from those governing gene expression in our model. Moreover, the general lack of overlap among the various datasets suggests that in the context of Pten disruption, there may be little redundancy in modes of dysregulation, meaning that molecules are either subject to dysregulation via expression or phosphorylation but not both.
Differentially expressed/phosphorylated molecules associate with major intracellular signaling cascades, neurological processes, and cancer
To understand the biological consequences of the observed changes in protein expression and phosphorylation in the Ptenm3m4/m3m4 cortex, we performed an IPA analysis on the proteomic and phosphoproteomic (pooled phospho-serine/threonine and phospho-tyrosine datasets) findings. The comprehensive summaries for all IPA analyses can be found in the supplemental information (Additional file 3). The top five most enriched canonical pathways at P14 and P40 for the proteome and phosphoproteome are shown in Fig. 3a and b, respectively. In the Ptenm3m4/m3m4 proteome, the top implicated canonical pathways broadly involve phosphoinositide metabolism (i.e., 3’-phosphoinositide Biosynthesis) or neurological pathways, such as GABA Receptor Signaling (Fig. 3a). In the Ptenm3m4/m3m4 phosphoproteome, the top implicated canonical pathways generally involve neurological and cancer processes or major cellular signaling cascades: Molecular Mechanisms of Cancer, Synaptic Long Term Potentiation, and cAMP-mediated Signaling (Fig. 3b). Moreover, we extracted the top two salient disease and bio function networks (i.e., the networks comprised of the largest number of differentially expressed/phosphorylated molecules) from the proteome and phosphoproteome at P40. Cancer and Development of Head are the two largest disease and bio function networks identified by IPA in the Ptenm3m4/m3m4 P40 cortical proteome (Fig. 3c), and Synaptic Transmission and Seizure Disorder are two of the largest salient networks from the Ptenm3m4/m3m4 P40 cortical phosphoproteome (Fig. 3d). These enrichment analyses highlight the broad yet related biological perturbations in the neural (phospho)proteome of Ptenm3m4/m3m4 mice.
In an effort to understand more about how these biological changes are initiated and maintained, we sought to identify important upstream regulators via network analysis of the differentially expressed proteins. Strikingly, the top protein interaction network identified by IPA from the Ptenm3m4/m3m4 P40 cortical proteome agnostically positions Pten as the pre-eminent regulatory node via hierarchical ordering. The position of Pten at the top of this network’s regulatory architecture establishes its importance and influence as a regulator of the molecules positioned below (Fig. 3e). When IPA’s biological predictions are overlaid on the network using the Molecule Activity Predictor (MAP) tool, the observed decrease in Pten expression predicts an increase in the activity of Pi3k, Akt, Erk, p70 S6, Gfap, and C1qa, while predicting a decrease in P38 Mapk and Creb (Fig. 3e). These predictions are largely consistent with previous observations in the Ptenm3m4/m3m4 cortex [21,23,24]. The network analysis enables a biological understanding of the changes in protein expression and phosphorylation, which unequivocally point to disrupted Pten function in the brain and highlight its importance in propagating dysfunction to downstream effector molecules.
Pten and Psd-95 are major regulatory nodes in the Ptenm3m4/m3m4cortex
In an effort to expand our biological understanding of the results from the proteomic and phosphoproteomic Ptenm3m4/m3m4 versus Pten+/+ comparisons, we performed STRING analyses on each to identify the relationships among the differentially expressed/phosphorylated molecules. We extracted the largest STRING networks from each –omic dataset at each time points and analyzed the network statistics using Cytoscape (Additional file 2: Fig. S2). The most striking network findings were observed in the P40 Ptenm3m4/m3m4 cortical proteome and phosphoproteome. In the largest network (83 nodes) constructed from the P40 Ptenm3m4/m3m4 cortical proteome findings, we found that Pten exhibited the greatest degree of connectivity, 12, and betweenness centrality, 0.66, relative to all other nodes (Fig. 4a). These data independently identify Pten as the most connected (i.e., degree connectivity) and most important node for transmitting information across the network (i.e., betweenness centrality), suggesting that Pten is likely to be the dominant regulatory node affecting the network of differentially expressed proteins in the P40 mutant cortex. Additionally, in the largest network (92 nodes) constructed from the P40 Ptenm3m4/m3m4 cortical phospho-serine/threonine scan, we found Psd-95 (also known as Dlg4) exhibited the greatest degree connectivity, 19, and betweenness centrality, 0.68, relative to all other nodes (Fig. 4b). These data identify Psd-95 as the most connected (i.e., highest degree connectivity) and most important node for transmitting information across the network (i.e., highest betweenness centrality), suggesting that Psd-95 is likely to be the dominant regulatory node affecting the network of differentially phosphorylated proteins in the P40 mutant cortex. The Psd-95 finding also implicates Pten given the known and well-described protein-protein relationship between the two . Ultimately, the STRING network analysis implicates Pten and Psd-95 as the likely perpetrators of the proteomic and phosphoproteomic dysregulation observed in the cortex of the Ptenm3m4/m3m4 model and possibly responsible for some of the pathological cellular, physiological, and behavioral phenotypes.
Meaningful overlap between differentially expressed or phosphorylated molecules and known ASD risk genes
We sought to assess whether the differentially expressed/phosphorylated molecules identified by our –omic surveys demonstrate significant overlap with known autism risk genes as curated by the Simons Foundation Autism Research Initiative (SFARI). Accordingly, we compared the SFARI catalogue of ASD risk genes with the gene lists of the significant results of –omic surveys (time points and phospho-serine/threonine/tyrosine pooled). We found that 41 molecules intersected between the phosphoproteome and SFARI genes, and 12 molecules intersected between the proteome and SFARI (Fig. 5a). This was modest overlap given the number of genes curated by SFARI, but again, STRING network analysis implicated Pten and Psd-95 as central nodes in association networks derived from the intersecting molecules (Fig. 5b, c). The re-emergence of Pten and Psd-95 in a separate network analysis predicated on known ASD risk genes strengthens the evidence that implicates them as potential drivers of the phenotypes observed in Ptenm3m4/m3m4 mouse model. The overlap that does exist with the known ASD risk genes underscores the importance of Pten biology to ASD pathophysiology overall.