Structural basis of histidine kinase autophosphorylation deduced by integrating genomics, molecular dynamics, and mutagenesis

International audienceSignal transduction proteins such as bacterial sensor histidine kinases, designed to transition between multiple conformations, are often ruled by unstable transient interactions making structural characterization of all functional states difficult. This study explored the inactive and signal-activated conformational states of the two catalytic domains of sensor histidine kinases, HisKA and HATPase. Direct coupling analyses, a global statistical inference approach, was applied to > 13,000 such domains from protein databases to identify residue contacts between the two domains. These contacts guided structural assembly of the domains using MAGMA, an advanced molecular dynamics docking method. The active conformation structure generated by MAGMA simultaneously accommodated the sequence derived residue contacts and the ATP-catalytic histidine contact. The validity of this structure was confirmed biologically by mutation of contact positions in the Bacillus subtilis sensor histidine kinase KinA and by restoration of activity in an inactive KinA(HisKA): KinD(HATPase) hybrid protein. These data indicate that signals binding to sensor domains activate sensor histidine kinases by causing localized strain and unwinding at the end of the C-terminal helix of the HisKA domain. This destabilizes the contact positions of the inactive conformation of the two domains, identified by previous crystal structure analyses and by the sequence analysis described here, inducing the formation of the active conformation. This study reveals that structures of unstable transient complexes of interacting proteins and of protein domains are accessible by applying this combination of cross-validating technologies

Rapid Phenotypic and Genomic Change in Response to Therapeutic Pressure in Prostate Cancer Inferred by High Content Analysis of Single Circulating Tumor Cells

<div><p>Timely characterization of a cancer's evolution is required to predict treatment efficacy and to detect resistance early. High content analysis of single Circulating Tumor Cells (CTCs) enables sequential characterization of genotypic, morphometric and protein expression alterations in real time over the course of cancer treatment. This concept was investigated in a patient with castrate-resistant prostate cancer progressing through both chemotherapy and targeted therapy. In this case study, we integrate across four timepoints 41 genome-wide copy number variation (CNV) profiles plus morphometric parameters and androgen receptor (AR) protein levels. Remarkably, little change was observed in response to standard chemotherapy, evidenced by the fact that a unique clone (A), exhibiting highly rearranged CNV profiles and AR+ phenotype was found circulating before and after treatment. However, clinical response and subsequent progression after targeted therapy was associated with the drastic depletion of clone A, followed by the sequential emergence of two distinct CTC sub-populations that differed in both AR genotype and expression phenotype. While AR- cells with flat or pseudo-diploid CNV profiles (clone B) were identified at the time of response, a new tumor lineage of AR+ cells (clone C) with CNV altered profiles was detected during relapse. We showed that clone C, despite phylogenetically related to clone A, possessed a unique set of somatic CNV alterations, including <i>MYC</i> amplification, an event linked to hormone escape. Interesting, we showed that both clones acquired <i>AR</i> gene amplification by deploying different evolutionary paths. Overall, these data demonstrate the timeframe of tumor evolution in response to therapy and provide a framework for the multi-scale analysis of fluid biopsies to quantify and monitor disease evolution in individual patients.</p></div

Abiraterone acetate induces phenotypic alterations in the CTC population.

<p>(A) The total HD-CTCs counts, including the number of phenotypically distinct AR⁺ and AR⁻ cells, was determined for each blood Draw collected during therapeutic intervention. CTCs were defined as AR positive if the AR signal intensity was higher than six standard deviations over the mean (SDOM) of the surrounding leukocytes (background). The bar-graph shows the change in the distribution of the AR⁺ and AR⁻ CTC subpopulations along the course of treatment, indicated in red and blue respectively, and the numbers are presented above each bar. (B) PSA concentration measured at each treatment timepoint. (C) Boxplot of cell roundness for each individual CTC identified across the different treatment timepoints. (D) Representative 40× immunofluorescence images of AR⁺ and AR⁻ HD-CTCs from the subpopulations identified in each treatment timepoint. Immunofluorescence channels are colored as follows: nucleus: blue; cytokeratin: red; AR: white; and CD45: green. AR phenotype is indicated in the bottom left corner of each image. All graphs were constructed using the ggplot2 and rgl packages in R.</p

Concurrent phenotypic and genotypic profiling of single prostate tumor cells.

<p>Copy number variation profiles from the patient's bone metastasis; a control single WBC; and single CTCs from each of the four treatment timepoints are shown. The corresponding fluorescent image of the cell used to generate the CNV profile is shown to the right. Relevant genomic alterations and their chromosome localizations occurring in each specific draw are indicated with pale blue bars.</p

AR subcellular localization changes at the time of disease progression.

<p>(A) Comparison of the AR subcellular localization in the CTCs identified in the blood prior to and after nine weeks of abiraterone treatment. Correlation between the AR and DAPI signals within the cell is indicative of AR being colocalized with DAPI, <i>i.e.</i> localized in the cell nucleus. High correlation was generally seen before abiraterone treatment, but a shift to less nuclear stain was observed after nine weeks of treatment (p = 0.00017, Wilcoxon sum-rank test). (B) and (D) Height maps constructed from the pixel intensities of CK (red), AR (green) and DAPI (blue) in representative CTCs to visualize the subcellular localization of AR. The cell in (B) was isolated before abiraterone initiation and displays AR staining confined to the nucleus, while cytoplasmic AR staining is observed in the CTC identified at the time of therapeutic relapse (D). (C) and (E) Plots of AR versus DAPI signal intensities for each pixel inside the cell in the 40× images of the CTCs in (B) and (D), respectively. Each plot point is colored by the corresponding CK signal intensity. Nuclear localization was observed as positive correlation between the two intensities (C), and nuclear exclusion as negative correlation (E). All graphs and were done using the ggplot2 and rgl packages in R.</p

Clonality and genomic aberrations in the CTC population.

<p>(A) Three different clonal lineages, represented as Cluster A, B and C, were identified based on the comparison of 41 single cell CNV profiles in an unsupervised hierarchical clustering. The blood draw from which each cell was isolated is indicated as Draw 1: yellow; Draw 2: orange; Draw 3: purple; and Draw 4: black. For reference, the bone metastasis FFPE tissue was included in the tree, colored in green. Below the tree, a heatmap indicates the amplifications (red) and deletions (blue) across the entire genome of each individual cell. (B) Frequency of genomic amplifications and deletions in the three clusters identified. Areas uniquely amplified (red) or deleted (blue) in cluster A and C are highlighted. (C) A detail plot of the AR amplification event colored per draw for each individual cluster is shown.</p