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Elevated protein concentrations in newborn blood and the risks of autism spectrum disorder, and of social impairment, at age 10 years among infants born before the 28th week of gestation
Among the 1 of 10 children who are born preterm annually in the United States, 6% are born before the third trimester. Among children who survive birth before the 28th week of gestation, the risks of autism spectrum disorder (ASD) and non-autistic social impairment are severalfold higher than in the general population. We examined the relationship between top quartile inflammation-related protein concentrations among children born extremely preterm and ASD or, separately, a high score on the Social Responsiveness Scale (SRS total score ≥65) among those who did not meet ASD criteria, using information only from the subset of children whose DAS-II verbal or non-verbal IQ was ≥70, who were assessed for ASD, and who had proteins measured in blood collected on ≥2 days (N = 763). ASD (N = 36) assessed at age 10 years is associated with recurrent top quartile concentrations of inflammation-related proteins during the first post-natal month (e.g., SAA odds ratio (OR); 95% confidence interval (CI): 2.5; 1.2–5.3) and IL-6 (OR; 95% CI: 2.6; 1.03–6.4)). Top quartile concentrations of neurotrophic proteins appear to moderate the increased risk of ASD associated with repeated top quartile concentrations of inflammation-related proteins. High (top quartile) concentrations of SAA are associated with elevated risk of ASD (2.8; 1.2–6.7) when Ang-1 concentrations are below the top quartile, but not when Ang-1 concentrations are high (1.3; 0.3–5.8). Similarly, high concentrations of TNF-α are associated with heightened risk of SRS-defined social impairment (N = 130) (2.0; 1.1–3.8) when ANG-1 concentrations are not high, but not when ANG-1 concentrations are elevated (0.5; 0.1–4.2)
Cardiolipin biosynthesis in Streptococcus mutans is regulated in response to external pH
Phosphoproteome Response to Dithiothreitol Reveals Unique Versus Shared Features of Saccharomyces cerevisiae Stress Responses
Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response
<div><p>Cells respond to stressful conditions by coordinating a complex, multi-faceted response that spans many levels of physiology. Much of the response is coordinated by changes in protein phosphorylation. Although the regulators of transcriptome changes during stress are well characterized in <i>Saccharomyces cerevisiae</i>, the upstream regulatory network controlling protein phosphorylation is less well dissected. Here, we developed a computational approach to infer the signaling network that regulates phosphorylation changes in response to salt stress. We developed an approach to link predicted regulators to groups of likely co-regulated phospho-peptides responding to stress, thereby creating new edges in a background protein interaction network. We then use integer linear programming (ILP) to integrate wild type and mutant phospho-proteomic data and predict the network controlling stress-activated phospho-proteomic changes. The network we inferred predicted new regulatory connections between stress-activated and growth-regulating pathways and suggested mechanisms coordinating metabolism, cell-cycle progression, and growth during stress. We confirmed several network predictions with co-immunoprecipitations coupled with mass-spectrometry protein identification and mutant phospho-proteomic analysis. Results show that the cAMP-phosphodiesterase Pde2 physically interacts with many stress-regulated transcription factors targeted by PKA, and that reduced phosphorylation of those factors during stress requires the Rck2 kinase that we show physically interacts with Pde2. Together, our work shows how a high-quality computational network model can facilitate discovery of new pathway interactions during osmotic stress.</p></div
Rewired cellular signaling coordinates sugar and hypoxic responses for anaerobic xylose fermentation in yeast.
Microbes can be metabolically engineered to produce biofuels and biochemicals, but rerouting metabolic flux toward products is a major hurdle without a systems-level understanding of how cellular flux is controlled. To understand flux rerouting, we investigated a panel of Saccharomyces cerevisiae strains with progressive improvements in anaerobic fermentation of xylose, a sugar abundant in sustainable plant biomass used for biofuel production. We combined comparative transcriptomics, proteomics, and phosphoproteomics with network analysis to understand the physiology of improved anaerobic xylose fermentation. Our results show that upstream regulatory changes produce a suite of physiological effects that collectively impact the phenotype. Evolved strains show an unusual co-activation of Protein Kinase A (PKA) and Snf1, thus combining responses seen during feast on glucose and famine on non-preferred sugars. Surprisingly, these regulatory changes were required to mount the hypoxic response when cells were grown on xylose, revealing a previously unknown connection between sugar source and anaerobic response. Network analysis identified several downstream transcription factors that play a significant, but on their own minor, role in anaerobic xylose fermentation, consistent with the combinatorial effects of small-impact changes. We also discovered that different routes of PKA activation produce distinct phenotypes: deletion of the RAS/PKA inhibitor IRA2 promotes xylose growth and metabolism, whereas deletion of PKA inhibitor BCY1 decouples growth from metabolism to enable robust fermentation without division. Comparing phosphoproteomic changes across ira2Δ and bcy1Δ strains implicated regulatory changes linked to xylose-dependent growth versus metabolism. Together, our results present a picture of the metabolic logic behind anaerobic xylose flux and suggest that widespread cellular remodeling, rather than individual metabolic changes, is an important goal for metabolic engineering
Inferred feedback in the PKA pathway.
<p>Shown are all submodules connected to at least one PKA catalytic subunit (‘PKA’) and containing a known regulator in the PKA pathway. Pink and purple filled circles represent the PKA catalytic subunits and members of the PKA pathway, respectively. Submodules are colored according to the key and annotated by the phospho-motif and mutant phenotype if peptide changes were defective (-) or amplified (+) in the <i>pde2</i>Δ (‘p’), or <i>cdc14-3</i> (‘c’) mutants. Inhibition of PKA by Pde2-dependent cAMP decay is also represented. Red arrows indicate a motif match between at least one PKA kinase subunit and the target submodule (FDR < 0.2). Ras2 is shown in its active (bound to GTP) and inactive states (bound to GDP). The predicted PKA auto-phosphorylation is on the Tpk3 subunit.</p
Subnetwork related to cell cycle control.
<p>A manually chosen region of the network capturing known regulation between Hog1 and Hsl1 (see text), other regulators connected to Hog1 or Hsl1, and all submodules connected to those regulators. Submodules are annotated by the phospho-motif and mutant phenotype if peptide changes were defective (-) or amplified (+) in the <i>hog1</i>Δ (‘h’), <i>pde2</i>Δ (‘p’), or <i>cdc14-3</i> (‘c’) mutants, and colored according to the key. Solid arrows represent directed SI-submodule edges or known directional interactions, dashed arrows represent directionality inferred by the ILP, and ball-and-stick edges indicate protein constituents of the submodule from which the line emits. Red arrows indicate a motif match between the known SI kinase specificity and the target submodule (FDR < 0.2). Asterisks denote submodules containing known Cdc28 target proteins, as curated in Chasman <i>et al</i> [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref052" target="_blank">52</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref053" target="_blank">53</a>], or phospho-peptides with defective phosphorylation in a strain in which Cdc28 was chemically inhibited [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref005" target="_blank">5</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref061" target="_blank">61</a>].</p
Pde2 interacts with stress-regulated transcription factors.
<p>Shown are 10 phospho-repressed submodules (blue rectangles) downstream of at least one PKA subunit and containing at least one transcription factor, as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.g004" target="_blank">Fig 4</a>. Dashed lines without arrows denote Pde2 protein interactions identified by co-IP. Bolded red text denotes factors that are either known or predicted PKA targets [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref052" target="_blank">52</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref053" target="_blank">53</a>] or reside in pathways directly regulated by PKA [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref083" target="_blank">83</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref085" target="_blank">85</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.ref087" target="_blank">87</a>].</p
Rck2 is a hub in the osmotic stress-signaling network.
<p>A manually chosen section of the network capturing source regulators, Hog pathway components, and Rck2 shown, as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006088#pcbi.1006088.g004" target="_blank">Fig 4</a> and the key. The figure also shows three constituent proteins that are predicted targets of our method and whose physical interaction with source regulators we validated by co-immunoprecipitation (dashed lines).</p
Overview of the inference method.
<p>The method consists of three main steps (see text for details). In the first step, stress-altered phospho-peptides are partitioned into submodules of peptides likely to be co-regulated. This is accomplished by <b>(A)</b> clustering phospho-peptides based on their pattern of phosphorylation change in wild type cells, then <b>(B)</b> further partitioning peptides into submodules if they share the same phospho-motif and <b>(C)</b> if they share the same defect in each of three interrogated mutant strains. Yellow and blue filled submodules are comprised of peptides with increased or decreased phosphorylation, respectively, in response to NaCl. <b>(D)</b> We then identify ‘Shared Interactors’ (SIs, green circles) as proteins that show more physical interaction with submodule constituent proteins than expected by chance–identified SIs are connected to each submodule with a new directional edge. <b>(E)</b> A background network of previously measured protein-protein (undirected dashed line) and kinase-substrate (directed arrow) interactions, represented here with Proteins A–L, is augmented by <b>(F)</b> adding SI-submodule units as well as outgoing edges (ball and stick) between each submodule and its constituent proteins whose phospho-peptides belong to the submodule. <b>(G)</b> The ILP method then enumerates all paths of a given length from each source regulator (red) to its dependent submodules (grey boxes), traversing through SIs (green) and other proteins in the augmented PPI background network. In this cartoon, submodules 1, 2, and 3 consist of phospho-peptides whose salt responsiveness depends on interrogated source regulator Protein F. Submodules without a source dependency (white boxes) can be incorporated as pathway intermediates. <b>(H)</b> The ILP connects the units using a multi-stage objective function to reveal the subnetwork inferred to regulate phosphoproteome changes.</p