Dynamic impacts of the inhibition of the molecular chaperone Hsp90 on the T-cell proteome have implications for anti-cancer therapy

Abstract

The molecular chaperone Hsp90-dependent proteome represents a complex protein network of critical biological and medical relevance. Known to associate with proteins with a broad variety of functions termed clients, Hsp90 maintains key essential and oncogenic signalling pathways. Consequently, Hsp90 inhibitors are being tested as anti-cancer drugs. Using an integrated systematic approach to analyse the effects of Hsp90 inhibition in T-cells, we quantified differential changes in the Hsp90-dependent proteome, Hsp90 interactome, and a selection of the transcriptome. Kinetic behaviours in the Hsp90-dependent proteome were assessed using a novel pulse-chase strategy (Fierro-Monti et al., accompanying article), detecting effects on both protein stability and synthesis. Global and specific dynamic impacts, including proteostatic responses, are due to direct inhibition of Hsp90 as well as indirect effects. As a result, a decrease was detected in most proteins that changed their levels, including known Hsp90 clients. Most likely, consequences of the role of Hsp90 in gene expression determined a global reduction in net de novo protein synthesis. This decrease appeared to be greater in magnitude than a concomitantly observed global increase in protein decay rates. Several novel putative Hsp90 clients were validated, and interestingly, protein families with critical functions, particularly the Hsp90 family and cofactors themselves as well as protein kinases, displayed strongly increased decay rates due to Hsp90 inhibitor treatment. Remarkably, an upsurge in survival pathways, involving molecular chaperones and several oncoproteins, and decreased levels of some tumour suppressors, have implications for anti-cancer therapy with Hsp90 inhibitors. The diversity of global effects may represent a paradigm of mechanisms that are operating to shield cells from proteotoxic stress, by promoting pro-survival and anti-proliferative functions. Data are available via ProteomeXchange with identifier PXD000537.

Figure 1. stSILAC experiments and workflow for data analysis.

A) Labelling and sample preparation scheme. Geldanamycin or DMSO were added at t = 0. Total protein extracts were collected at t = 6h and 20h, while total mRNA was taken at t = 5h and t = 19h. B) Data analysis and interpretation combined data on protein abundance changes (stSILAC) with protein-protein interactions (PPI) analysed as a network, synthesis and decay values for proteins in the two conditions and transcript levels. Enrichment of Gene Ontology annotation terms was used to extract functional information on protein categories with common behaviours.

Figure 2. Distribution of stSILAC quantitative ratios at two time points of GA treatment and clustering of proteins with significant changes.

A) Histograms of normalized treated/control ratios for the stSILAC 6h (yellow), and 20h (green) datasets (4050 proteins). B) Twelve possible patterns of change upon GA treatment were defined (small plots) as models for correlation clustering. Additional clusters (not shown) included proteins with no significant changes (cluster 13) and proteins with data only at one time point (cluster 14). The number of genes (encoding proteins) identified in each cluster are indicated in the box.

Figure 3. GA-induced remodeling of the Hsp90 chaperone and protein degradation machineries.

A) Components of the Hsp90 molecular chaperone machine (Hsp90Int, [21]) showing significant changes in the stSILAC data are schematized in a graph. Edges (lines) represent protein-protein interactions amongst members of the machinery. stSILAC data is integrated in the graph and represented as a colour gradient (red corresponds to enrichment, white is no change and blue is depletion) (see legend). B) GA-induced changes of the proteasomal/ubiquitination machinery with connected Hsp90 clients. Members of the proteasomal complexes, ubiquitination machinery, molecular chaperones and known or potential Hsp90 client proteins are interconnected by edges indicating protein-protein interactions. Relative levels of proteins at 6h and at 20h after GA treatment are integrated in the graph and represented as a colour gradient (red corresponds to enrichment, white is no change and blue is depletion) (see legend).

Figure 4. Both beneficial and detrimental effects of Hsp90 inhibitors on cancer-related proteins.

Cancer proteins categorized by Higgins et al. as oncogenes or tumour suppressors were retrieved and identified in the stSILAC data. These results were further refined and confirmed by literature mining, and organized in a protein-protein interaction network. Relative levels of proteins at 20hs after GA treatment are integrated in the graph and represented by the same colour gradient as in Fig. 2.

Figure 5. Validation of new Hsp90 clients.

A) Network analysis of a selected set of potentially new Hsp90 client proteins. stSILAC data and the Hsp90 interaction network Hsp90Int were combined to identify interesting candidates (OGT, ITK and BRAT1) with no reported interactions with Hsp90 at the time of the analysis. Edges connecting candidate proteins with known Hsp90 interacting proteins are highlighted in red. B). Co-immunoprecipitation (co-IP) experiment demonstrating interactions between BRAT1, OGT and ITK with Hsp90β in Jurkat cells. Equal concentrations of specific antibodies against BRAT1 (rabbit), OGT (rabbit), ITK (mouse), Hsp90β (mouse), and the corresponding non-immune (NI) control antibodies from rabbit and mouse were used in co-IP experiments, and then analysed by immunoblotting (WB). C) GA-induced degradation of BRAT1, ITK and OGT in Jurkat cells. Lysates from cells treated with GA or with the equivalent volume of the solvent DMSO (control) for 6 and 20 h were analysed by WB for these three mentioned proteins and also for Hsp70 and CDK6 as positive controls of GA action.

Figure 6. Changes in decay rate constants, synthesis rates, abundance and half-life for protein categories in response to treatment with geldanamycin.

Relative average changes in synthesis (log₂ [V_{s_GA}/V_{s_DMSO}]), decay (log₂ [k_{d_GA}/k_{d_DMSO}]), abundance (log₂ [p_GA/p_DMSO]), and half-life (log₂ [T_{1/2_GA}/T_{1/2_DMSO}]) for selected protein categories. Numbers of proteins in each category are indicated in brackets. All values shown are adjusted for global proteome changes in synthesis and decay by subtracting the median of ratios for the whole dataset (911 proteins).

Figure 7. Relative changes in kinetic parameters upon GA treatment.

Relative changes in synthesis (log₂ [V_{s_GA}/V_{s_DMSO}]), decay (log₂ [k_{d_GA}/k_{d_DMSO}]), abundance (log₂ [p_GA/p_DMSO]), and half-life, (log₂ [T_{1/2_GA}/T_{1/2_DMSO}]) for selected proteins are represented in the respective plots according to the bar colour codes. Proteins were selected from the following categories: A) Hsp90 and cofactors B) Protein kinases and C) Tumour suppressors and oncogenes. As for Figure 6, all values are adjusted by the median of the entire proteome. PRKAR1 and FASN are annotated as both possible oncogenes and tumor suppressors .

Figure 8. Dynamic changes in mRNA levels and net protein abundances (stSILAC), protein synthesis, or decay (pcSILAC) rates for a selection of transcripts/proteins from several clusters upon Hsp90 inhibition.

Plots show data derived from stSILAC (A and B) and from pcSILAC experiment 2 (C, D, E, and F). A) Variations in mRNA (log₂ fold-change) versus protein levels at 5-6h. B) Variations in mRNA (log₂ fold-change) versus protein levels at 19-20h. C) Plot describing the variations in mRNA (log₂ fold-change) at 5h versus protein synthesis rates (log₂ [V_{s_GA}/V_{s_DMSO}]). D) same as C) but mRNA at t = 19h. E) Plots describing the variations in mRNA (log₂ fold-change) at 5h versus decay rates (log₂ [k_{s_GA}/k_{s_DMSO}]). F) same as E) but at t = 19-20h. A selection of transcripts encoding Hsp90, cofactor DNAJB1, Hsp90 clients Cdk6, Lck, FASN and the novel potential Hsp90 client ITK, are labelled.