Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome.

We have previously reported that in thrombin-stimulated human platelets, cytosolic phospholipase A 2 (cPLA2) is phosphorylated on Ser-505 by p38 protein kinase and on Ser-727 by an unknown kinase. Pharmacological inhibition of p38 leads to inhibition of cPLA2 phosphorylation at both Ser-505 and Ser-727 suggesting that the kinase responsible for phosphorylation on Ser-727 is activated in a p38-dependent pathway. By using Chinese hamster ovary, HeLa, and HEK293 cells stably transfected with wild type and phosphorylation site mutant forms of cPLA2, we show that phosphorylation of cPLA2 at both Ser-505 and Ser-727 and elevation of Ca 2؉ leads to its activation in agonist-stimulated cells. The p38-activated protein kinases MNK1, MSK1, and PRAK1 phosphorylate cPLA2 in vitro uniquely on Ser-727 as shown by mass spectrometry. Furthermore, MNK1 and PRAK1, but not MSK1, is present in platelets and undergo modest activation in response to thrombin. Expression of a dominant negative form of MNK1 in HEK293 cells leads to significant inhibition of cPLA2-mediated arachidonate release. The results suggest that MNK1 or a closely related kinase is responsible for in vivo phosphorylation of cPLA2 on Ser-727.

We analyzed a recently reported (K. Seno, T. Okuno, K. Nishi, Y. Murakami, F. Watanabe, T. Matsuur, M. Wada, Y. Fujii, M. Yamada, T. Ogawa, T. Okada, H. Hashizume, M. Kii, S.-H. Hara, S. Hagishita, S. Nakamoto, J. Med. Chem. 43 (2000)) pyrrolidine-based inhibitor, pyrrolidine-1, against the human group IV cytosolic phospholipase A(2) alpha-isoform (cPLA(2)alpha). Pyrrolidine-1 inhibits cPLA(2)alpha by 50% when present at approx. 0.002 mole fraction in the interface in a number of in vitro assays. It is much less potent on the cPLA(2)gamma isoform, calcium-independent group VI PLA(2) and groups IIA, X, and V secreted PLA(2)s. Pyrrolidine-1 blocked all of the arachidonic acid released in Ca(2+) ionophore-stimulated CHO cells stably transfected with cPLA(2)alpha, in zymosan- and okadaic acid-stimulated mouse peritoneal macrophages, and in ATP- and Ca(2+) ionophore-stimulated MDCK cells.

Catalysis using iron–sulfur clusters and transition metals can be traced back to the last universal common ancestor. The damage to metalloproteins caused by reactive oxygen species (ROS) can prevent cell growth and survival when unmanaged, thus eliciting an essential stress response that is universal and fundamental in biology. Here we develop a computable multiscale description of the ROS stress response inEscherichia coli, called OxidizeME. We use OxidizeME to explain four key responses to oxidative stress: 1) ROS-induced auxotrophy for branched-chain, aromatic, and sulfurous amino acids; 2) nutrient-dependent sensitivity of growth rate to ROS; 3) ROS-specific differential gene expression separate from global growth-associated differential expression; and 4) coordinated expression of iron–sulfur cluster (ISC) and sulfur assimilation (SUF) systems for iron–sulfur cluster biosynthesis. These results show that we can now develop fundamental and quantitative genotype–phenotype relationships for stress responses on a genome-wide basis.