Binding of the HSF-1 DNA-binding domain to multimeric C. elegans consensus HSEs is guided by cooperative interactions.

The protein HSF-1 is the controlling transcription factor of the heat-shock response (HSR). Its binding to the heat-shock elements (HSEs) induces the strong upregulation of conserved heat-shock proteins, including Hsp70s, Hsp40s and small HSPs. Next to these commonly known HSPs, more than 4000 other HSEs are found in the promoter regions of C. elegans genes. In microarray experiments, few of the HSE-containing genes are specifically upregulated during the heat-shock response. Most of the 4000 HSE-containing genes instead are unaffected by elevated temperatures and coexpress with genes unrelated to the HSR. This is also the case for several genes related to the HSP chaperone system, like dnj-12, dnj-13, and hsp-1. Interestingly, several promoters of the dedicated HSR-genes, like F44E5.4p, hsp-16.48p or hsp-16.2p, contain extended HSEs in their promoter region, composed of four or five HSE-elements instead of the common trimeric HSEs. We here aim at understanding how HSF-1 interacts with the different promoter regions. To this end we purify the nematode HSF-1 DBD and investigate the interaction with DNA sequences containing these regions. EMSA assays suggest that the HSF-1 DBD interacts with most of these HSE-containing dsDNAs, but with different characteristics. We employ sedimentation analytical ultracentrifugation (SV-AUC) to determine stoichiometry, affinity, and cooperativity of HSF-1 DBD binding to these HSEs. Interestingly, most HSEs show cooperative binding of the HSF-1 DBD with up to five DBDs being bound. In most cases binding to the HSEs of inducible promoters is stronger, even though the consensus scores are not always higher. The observed high affinity of HSF-1 DBD to the non-inducible HSEs of dnj-12, suggests that constitutive expression may be supported from some promoter regions, a fact that is evident for this transcription factor, that is essential also under non-stress conditions.

HSP-90/kinase complexes are stabilized by the large PPIase FKB-6.

Protein kinases are important regulators in cellular signal transduction. As one major type of Hsp90 client, protein kinases rely on the ATP-dependent molecular chaperone Hsp90, which maintains their structure and supports their activation. Depending on client type, Hsp90 interacts with different cofactors. Here we report that besides the kinase-specific cofactor Cdc37 large PPIases of the Fkbp-type strongly bind to kinase•Hsp90•Cdc37 complexes. We evaluate the nucleotide regulation of these assemblies and identify prominent interaction sites in this quaternary complex. The synergistic interaction between the participating proteins and the conserved nature of the interaction suggests functions of the large PPIases Fkbp51/Fkbp52 and their nematode homolog FKB-6 as contributing factors to the kinase cycle of the Hsp90 machinery.

head-bent resistant Hsc70 variants show reduced Hsp40 affinity and altered protein folding activity.

The molecular chaperone Hsc70 performs essential tasks by folding proteins. Hsc70 is driven by the hydrolysis of ATP and tuned by the association with various co-chaperones. One such cofactor is the nematode nucleotide exchange factor UNC-23, whose mutation disrupts muscle attachment and induces a severe head-bent phenotype in C.elegans. Interestingly, four mutations in Hsc70 can suppress this phenotype, but the molecular mechanism underlying this suppression is unknown. Here we characterize these four suppressor variants, Hsc70 D233N, S321F, A379V and D384N. In vitro only Hsc70 S321F shows reduced stability and altered nucleotide interaction, but all mutations affect the ATPase stimulation. In particular, Hsc70 D233N and Hsc70 A379V show strongly reduced interactions with DNJ-12 and DNJ-13. Nucleotide exchange factor binding instead is barely influenced in Hsc70 D233N, A379V and D384N and their chaperone activity is preserved. Molecular dynamics simulations suggest that effects in Hsc70 S321F and Hsc70 A379V originate from steric clashes in the vicinity of the mutation site, while D233N disrupts a salt bridge that contributes to Hsc70's nucleotide-induced conformational changes. In summary, the analyzed mutants show altered ATPase and refolding activity caused by changes in Hsp40 binding.

Genome-wide analysis of yeast expression data based on a priori generated co-regulation cliques.

DNA microarrays are highly sensitive tools to evaluate the gene expression status of organismic samples and standardized array formats exist for many different sample types. Differential expression studies usually utilize the strongest upor downregulated genes to generate networks visualizing the relationships among these genes. To include all yeast genes in one analysis and to get broader information on all cellular responses, we test a priori input of predefined genome-wide expression cliques and subsequent statistical analysis of the expression data. To this end, we generate a set of 72 co-regulation cliques using the information from 3196 microarray experiments. The obtained cliques performed highly significant in gene ontology and transcription factor enrichment analyses. We then tested the clique set on individual microarray experiments reporting on responses to pheromone, glycerol versus glucose based growth and the cellular response to heat. In all cases a highly significant determination of affected expression cliques was possible based on their average expression differences, the positions of their genes within hit rankings (UpRegScore) or the enrichment of the Top200 hits in certain cliques. The 72 cliques were finally used to compare experiments, which reported on the transcriptional response to polyglutamine proteins of different lengths. Using the predefined clique set it is possible to identify with high sensitivity and good significance sample and condition specific changes to gene expression. We thus conclude that an analysis, starting with these 72 preformed expression cliques, can complement traditional microarray analyses by visualizing the entire response on a static genome-wide gene set.

Regulation of the Hsp90 system.

Hsp90 is a highly conserved and abundant chaperone. It participates in essential cellular activities by supporting the maturation process of its client proteins, many of which are protein kinases and steroid receptors. Client processing is achieved via extensive conformational changes within the dimeric chaperone. This requires an ATP hydrolysis activity that is controlled by auto-inhibitory mechanisms and several structurally diverse cofactors. Especially the client-specificity of Hsp90 depends on client-specific cofactors, which can adapt Hsp90's activities to the client requirements at different conditions and in different cell types. Additionally, post-translational modifications can influence almost every aspect of Hsp90's interactions and activities. In this review, we present these regulatory principles, discuss the factors that have an impact on Hsp90's function and elaborate the mechanisms that are responsible for regulating the Hsp90 machinery.

Hsp90-downregulation influences the heat-shock response, innate immune response and onset of oocyte development in nematodes.

Hsp90 is a molecular chaperone involved in the regulation and maturation of kinases and transcription factors. In Caenorhabditis elegans, it contributes to the development of fertility, maintenance of muscle structure, the regulation of heat-shock response and dauer state. To understand the consequences of Hsp90-depletion, we studied Hsp90 RNAi-treated nematodes by DNA microarrays and mass spectrometry. We find that upon development of phenotypes the levels of chaperones and Hsp90 cofactors are increased, while specific proteins related to the innate immune response are depleted. In microarrays, we further find many differentially expressed genes related to gonad and larval development. These genes form an expression cluster that is regulated independently from the immune response implying separate pathways of Hsp90-involvement. Using fluorescent reporter strains for the differentially expressed immune response genes skr-5, dod-24 and clec-60 we observe that their activity in intestinal tissues is influenced by Hsp90-depletion. Instead, effects on the development are evident in both gonad arms. After Hsp90-depletion, changes can be observed in early embryos and adults containing fluorescence-tagged versions of SEPA-1, CAV-1 or PUD-1, all of which are downregulated after Hsp90-depletion. Our observations identify molecular events for Hsp90-RNAi induced phenotypes during development and immune responses, which may help to separately investigate independent Hsp90-influenced processes that are relevant during the nematode's life and development.

Construction and evaluation of yeast expression networks by database-guided predictions.

DNA-Microarrays are powerful tools to obtain expression data on the genome-wide scale. We performed microarray experiments to elucidate the transcriptional networks, which are up- or down-regulated in response to the expression of toxic polyglutamine proteins in yeast. Such experiments initially generate hit lists containing differentially expressed genes. To look into transcriptional responses, we constructed networks from these genes. We therefore developed an algorithm, which is capable of dealing with very small numbers of microarrays by clustering the hits based on co-regulatory relationships obtained from the SPELL database. Here, we evaluate this algorithm according to several criteria and further develop its statistical capabilities. Initially, we define how the number of SPELL-derived co-regulated genes and the number of input hits influences the quality of the networks. We then show the ability of our networks to accurately predict further differentially expressed genes. Including these predicted genes into the networks improves the network quality and allows quantifying the predictive strength of the networks based on a newly implemented scoring method. We find that this approach is useful for our own experimental data sets and also for many other data sets which we tested from the SPELL microarray database. Furthermore, the clusters obtained by the described algorithm greatly improve the assignment to biological processes and transcription factors for the individual clusters. Thus, the described clustering approach, which will be available through the ClusterEx web interface, and the evaluation parameters derived from it represent valuable tools for the fast and informative analysis of yeast microarray data.