Stressed cells coordinate a multi-faceted response spanning many levels of physiology. Yet knowledge of the complete stress-activated regulatory network as well as design principles for signal integration remains incomplete. We developed an experimental and computational approach to integrate available protein interaction data with gene fitness contributions, mutant transcriptome profiles, and phospho-proteome changes in cells responding to salt stress, to infer the salt-responsive signaling network in yeast. The inferred subnetwork presented many novel predictions by implicating new regulators, uncovering unrecognized crosstalk between known pathways, and pointing to previously unknown ‘hubs’ of signal integration. We exploited these predictions to show that Cdc14 phosphatase is a central hub in the network and that modification of RNA polymerase II coordinates induction of stress-defense genes with reduction of growth-related transcripts. We find that the orthologous human network is enriched for cancer-causing genes, underscoring the importance of the subnetwork's predictions in understanding stress biology.

Healthy cells utilize intricate systems to monitor their environment and mount robust responses in the event of cellular stress. Whether stress arises from external insults or defects due to mutation and disease, cells must be able to respond precisely to mount the appropriate defenses. Multi-faceted stress responses are generally coupled with arrest of growth and cell-cycle progression, which both limits the transmission of damaged materials and serves to reallocate limited cellular resources toward defense. Therefore, stress defense versus rapid growth represent competing interests in the cell. How eukaryotic cells set the balance between defense versus proliferation, and in particular knowledge of the regulatory networks that control this decision, are poorly understood. In this perspective, we expand upon our recent work inferring the stress-activated signaling network in budding yeast, which captures pathways controlling stress defense and regulators of growth and cell-cycle progression. We highlight similarities between the yeast and mammalian stress responses and explore how stress-activated signaling networks in yeast can inform on signaling defects in human cancers.

Ionic copper entering blood plasma binds tightly to albumin and the macroglobulin transcuprein. It then goes primarily to the liver and kidney except in lactation, where a large portion goes directly to the mammary gland. Little is known about how this copper is taken up from these plasma proteins. To examine this, the kinetics of uptake from purified human albumin and alpha(2)-macroglobulin, and the effects of inhibitors, were measured using human hepatic (HepG2) and mammary epithelial (PMC42) cell lines. At physiological concentrations (3-6 muM), both cell types took up copper from these proteins independently and at rates similar to each other and to those for Cu-dihistidine or Cu-nitrilotriacetate (NTA). Uptakes from alpha(2)-macroglobulin indicated a single saturable system in each cell type, but with different kinetics, and 65-80% inhibition by Ag(I) in HepG2 cells but not PMC42 cells. Uptake kinetics for Cu-albumin were more complex and also differed with cell type (as was the case for Cu-histidine and NTA), and there was little or no inhibition by Ag(I). High Fe(II) concentrations (100-500 microM) inhibited copper uptake from albumin by 20-30% in both cell types and that from alpha(2)-macroglobulin by 0-30%, and there was no inhibition of the latter by Mn(II) or Zn(II). We conclude that the proteins mainly responsible for the plasma-exchangeable copper pool deliver the metal to mammalian cells efficiently and by several different mechanisms. alpha(2)-Macroglobulin delivers it primarily to copper transporter 1 in hepatic cells but not mammary epithelial cells, and additional as-yet-unidentified copper transporters or systems for uptake from these proteins remain to be identified.

Stress tolerance and rapid growth are often competing interests in cells. Upon severe environmental stress, many organisms activate defense systems concurrent with growth arrest. There has been debate as to whether aspects of the stress-activated transcriptome are regulated by stress or an indirect byproduct of reduced proliferation. For example, stressed Saccharomyces cerevisiae cells mount a common gene expression program called the environmental stress response (ESR) [1] comprised of ∼300 induced (iESR) transcripts involved in stress defense and ∼600 reduced (rESR) mRNAs encoding ribosomal proteins (RPs) and ribosome biogenesis factors (RiBi) important for division. Because ESR activation also correlates with reduced growth rate in nutrient-restricted chemostats and prolonged G1 in slow-growing mutants, an alternate proposal is that the ESR is simply a consequence of reduced division [2-5]. A major challenge is that past studies did not separate effects of division arrest and stress defense; thus, the true responsiveness of the ESR-and the purpose of stress-dependent rESR repression in particular-remains unclear. Here, we decoupled cell division from the stress response by following transcriptome, proteome, and polysome changes in arrested cells responding to acute stress. We show that the ESR cannot be explained by changes in growth rate or cell-cycle phase during stress acclimation. Instead, failure to repress rESR transcripts reduces polysome association of induced transcripts, delaying production of their proteins. Our results suggest that stressed cells alleviate competition for translation factors by removing mRNAs and ribosomes from the translating pool, directing translational capacity toward induced transcripts to accelerate protein production.

The carboxyl-terminal domain (CTD) of the largest subunit of RNA polymerase II (Pol II) orchestrates dynamic recruitment of specific cellular machines during different stages of transcription. Signature phosphorylation patterns of Y 1 S 2 P 3 T 4 S 5 P 6 S 7 heptapeptide repeats of the CTD engage specific "readers." Whereas phospho-Ser5 and phospho-Ser2 marks are ubiquitous, phospho-Thr4 is reported to only impact specific genes. Here, we identify a role for phospho-Thr4 in transcription termination at noncoding small nucleolar RNA (snoRNA) genes. Quantitative proteomics reveals an interactome of known readers as well as protein complexes that were not known to rely on Thr4 for association with Pol II. The data indicate a key role for Thr4 in engaging the machinery used for transcription elongation and termination. We focus on Rtt103, a protein that binds phosphoSer2 and phospho-Thr4 marks and facilitates transcription termination at protein-coding genes. To elucidate how Rtt103 engages two distinct CTD modifications that are differentially enriched at noncoding genes, we relied on NMR analysis of Rtt103 in complex with phospho-Thr4-or phospho-Ser2-bearing CTD peptides. The structural data reveal that Rtt103 interacts with phospho-Thr4 in a manner analogous to its interaction with phospho-Ser2-modified CTD. The same set of hydrogen bonds involving either the oxygen on phospho-Thr4 and the hydroxyl on Ser2, or the phosphate on Ser2 and the Thr4 hydroxyl, can be formed by rotation of an arginine side chain, leaving the intermolecular interface otherwise unperturbed. This economy of design enables Rtt103 to engage Pol II at distinct sets of genes with differentially enriched CTD marks.ach stage of transcription relies on ordered recruitment and exchange of specific protein complexes that act on RNA polymerase II, its nascent transcripts, and the underlying chromatin. This dynamic process is orchestrated via patterned posttranslational modifications of the carboxyl-terminal domain (CTD). This unusual and essential domain of Rpb1, the largest component of the 12-subunit polymerase, consists of repeating Y 1 S 2 P 3 T 4 S 5 P 6 S 7 heptapeptides (26 repeats in budding yeast and 52 in humans) (1). The mechanistic consequences of phosphorylating Ser5 and Ser2 have been well documented (2-11). However, the role of Thr4 phosphorylation (pThr4), and even the necessity of Thr4 for cellular survival, appears to differ among closely related species and between growth conditions within a given species (12-15). Recent mass spectrometric analysis of an extensively engineered CTD revealed a paucity of pThr4, raising questions about the importance of this mark (16). In contrast, similar studies found pThr4 marks at levels comparable to or greater than the ubiquitously placed pSer2 mark in both yeast and human cells (17). Thus, much remains to be understood about the natural abundance and functional role of pThr4 marks on the endogenous, unmodified CTD.Previous studies suggest that pThr4 has roles in transcriptional elongation, 3ʹ-...

Phosphorylation of the carboxyl-terminal domain (CTD) of the largest subunit of RNA polymerase II (Pol II) governs stage-specific interactions with different cellular machines. The CTD consists of Y 1 S 2 P 3 T 4 S 5 P 6 S 7 heptad repeats, and sequential phosphorylations of Ser7, Ser5 and Ser2 occur universally across Pol II-transcribed genes. Phosphorylation of Thr4, however, appears to selectively modulate transcription of specific classes of genes. Here, we identify 10 new Thr4 kinases from different kinase structural groups. Irreversible chemical inhibition of the most active Thr4 kinase, Hrr25, reveals a novel role for this kinase in transcription termination of specific class of noncoding snoRNA genes. Genome-wide profiles of Hrr25 reveal a selective enrichment at 3ʹ regions of noncoding genes that display termination defects. Importantly, phospho-Thr4 marks placed by Hrr25 are recognized by Rtt103, a key component of the termination machinery. Our results suggest that these uncommon CTD kinases selectively place phospho-Thr4 marks to regulate expression of targeted genes.

Normally Cu(II) entering the blood plasma binds tightly to albumin (Alb) and the macroglobulin, transcuprein. However, little is known about how this copper is then taken up by hepatic cells, which are main recipients. Two determine this, the kinetics of copper uptake from these plasma proteins, and effects of inhibitors, were measured in the human hepatic (HepG2) and mammary epithelial (PMC42) cell lines. At physiological concentrations (1–5 uM) both cell types took up Cu from purified human Alb and alpha‐2‐macroglobulin (a2M) at rates similar to each other and to those for Cu‐di‐histidine (His). Uptakes from a2M but not from Alb or His obeyed Michaelis Menten kinetics, with Kms of 4–6 uM. Uptake from a2M but not the others was markedly inhibited by Ag(I) in HepG2 but not PMC42 cells. Copper uptake was not inhibited by Fe(II), Mn(II), or Zn(II) except at very high concentrations (200–500 uM). We conclude that the two plasma proteins mainly responsible for the plasma exchangeable copper pool deliver copper as efficiently as amino acid complexes to mammalian cells; that a2M delivers it primarily to CTR1 in hepatic but not mammary epithelial cells; and that additional as yet unidentified transporters responsible for copper uptake from these plasma proteins remain to be identified. Supported by PHS Grant No. RO1 HD 46949.

Advances in systems biology have made clear the importance of network models for capturing knowledge about complex relationships in gene regulation, metabolism, and cellular signaling. A common approach to uncovering biological networks involves performing perturbations on elements of the network, such as gene knockdown experiments, and measuring how the perturbation affects some reporter of the process under study. In this article, we develop context-specific nested effects models (CSNEMs), an approach to inferring such networks that generalizes nested effects models (NEMs). The main contribution of this work is that CSNEMs explicitly model the participation of a gene in multiple contexts, meaning that a gene can appear in multiple places in the network. Biologically, the representation of regulators in multiple contexts may indicate that these regulators have distinct roles in different cellular compartments or cell cycle phases. We present an evaluation of the method on simulated data as well as on data from a study of the sodium chloride stress response in Saccharomyces cerevisiae.