Romano T. Kroemer

AIF and cyclophilin A cooperate in apoptosis-associated chromatinolysis

Cyclophilin A (CypA) was determined to interact with apoptosis-inducing factor (AIF) by mass spectroscopy, coimmunoprecipitation, pull-down assays, and molecular modeling. During the initial, caspase-independent stage of chromatin condensation that accompanies apoptosis, AIF and CypA were found to coimmunolocalize in the nucleus. Recombinant AIF and CypA proteins synergized in vitro in the degradation of plasmid DNA, as well as in the capacity to induce DNA loss in purified nuclei. The apoptogenic cooperation between AIF and CypA did not rely on the CypA peptidyl-prolyl cis–trans isomerase activity. In Cyp-expressing cells, AIF overexpression augmented apoptotic chromatinolysis. The AIF-dependent large-scale DNA fragmentation was less pronounced in CypA knockout cells as compared to controls. AIF mutants lacking the CypA-binding domain were inefficient apoptosis sensitizers in transfection experiments. Moreover, AIF failed to sensitize CypA knockout cells to apoptosis induction, and this defect in the AIF response was reversed by reintroduction of the CypA gene into CypA-deficient cells. In summary, AIF and CypA collaborate in chromatinolysis.

Association and dissociation of autophagy, apoptosis and necrosis by systematic chemical study.

To address the question of whether established or experimental anticancer chemotherapeutics can exert their cytotoxic effects by autophagy, we performed a high-content screen on a set of cytotoxic agents. We simultaneously determined parameters of autophagy, apoptosis and necrosis on cells exposed to ∼1400 compounds. Many agents induced a ‘pure’ autophagic, apoptotic or necrotic phenotype, whereas less than 100 simultaneously induced autophagy, apoptosis and necrosis. A systematic analysis of the autophagic flux induced by the most potent 80 inducers of GFP-LC3 puncta among the NCI panel agents showed that 59 among them truly induced autophagy. The remaining 21 compounds were potent inducers of apoptosis or necrosis, yet failed to stimulate an autophagic flux, which were characterized as microtubule inhibitors. Knockdown of ATG7 was efficient in preventing GFP-LC3 puncta, yet failed to attenuate cell death by the agents that induce GFP-LC3 puncta. Thus there is not a single compound that would induce cell death by autophagy in our screening, underscoring the idea that cell death is rarely, if ever, executed by autophagy in human cells.

Cytoplasmic STAT3 represses autophagy by inhibiting PKR activity.

In a screen designed to identify novel inducers of autophagy, we discovered that STAT3 inhibitors potently stimulate the autophagic flux. Accordingly, genetic inhibition of STAT3 stimulated autophagy in vitro and in vivo, while overexpression of STAT3 variants, encompassing wild-type, nonphosphorylatable, and extranuclear STAT3, inhibited starvation-induced autophagy. The SH2 domain of STAT3 was found to interact with the catalytic domain of the eIF2α kinase 2 EIF2AK2, best known as protein kinase R (PKR). Pharmacological and genetic inhibition of STAT3 stimulated the activating phosphorylation of PKR and consequent eIF2α hyperphosphorylation. Moreover, PKR depletion inhibited autophagy as initiated by chemical STAT3 inhibitors or free fatty acids like palmitate. STAT3-targeting chemicals and palmitate caused the disruption of inhibitory STAT3-PKR interactions, followed by PKR-dependent eIF2α phosphorylation, which facilitates autophagy induction. These results unravel an unsuspected mechanism of autophagy control that involves STAT3 and PKR as interacting partners.

Inhibition of adenine nucleotide translocator pore function and protection against apoptosis in vivo by an HIV protease inhibitor

Inhibitors of HIV protease have been shown to have antiapoptotic effects in vitro, yet whether these effects are seen in vivo remains controversial. In this study, we have evaluated the impact of the HIV protease inhibitor (PI) nelfinavir, boosted with ritonavir, in models of nonviral disease associated with excessive apoptosis. In mice with Fas-induced fatal hepatitis, Staphylococcal enterotoxin B-induced shock, and middle cerebral artery occlusion-induced stroke, we demonstrate that PIs significantly reduce apoptosis and improve histology, function, and/or behavioral recovery in each of these models. Further, we demonstrate that both in vitro and in vivo, PIs block apoptosis through the preservation of mitochondrial integrity and that in vitro PIs act to prevent pore function of the adenine nucleotide translocator (ANT) subunit of the mitochondrial permeability transition pore complex.

Assessment of docking poses: Interactions-based accuracy classification (IBAC) versus crystal structure deviations

Six docking programs (FlexX, GOLD, ICM, LigandFit, the Northwestern University version of DOCK, and QXP) were evaluated in terms of their ability to reproduce experimentally observed binding modes (poses) of small-molecule ligands to macromolecular targets. The accuracy of a pose was assessed in two ways: First, the RMS deviation of the predicted pose from the crystal structure was calculated. Second, the predicted pose was compared to the experimentally observed one regarding the presence of key interactions with the protein. The latter assessment is referred to as interactions-based accuracy classification (IBAC). In a number of cases significant discrepancies were found between IBAC and RMSD-based classifications. Despite being more subjective, the IBAC proved to be a more meaningful measure of docking accuracy in all these cases.

Physical interaction of apoptosis-inducing factor with DNA and RNA

Apoptosis-inducing factor (AIF) is a mitochondrial flavoprotein, which upon apoptosis induction translocates to the nucleus where it interacts with DNA by virtue of positive charges clustered on the AIF surface. Here we show that the AIF interactome, as determined by mass spectroscopy, contains a large panel of ribonucleoproteins, which apparently bind to AIF through the RNA moiety. However, AIF is devoid of any detectable RNAse activity both in vitro and in vivo. Recombinant AIF can directly bind to DNA as well as to RNA. This binding can be visualized by electron microscopy, revealing that AIF can condense DNA, showing a preferential binding to single-stranded over double-stranded DNA. AIF also binds and aggregates single-stranded and structured RNA in vitro. Single-stranded poly A, poly G and poly C, as well double-stranded A/T and G/C RNA competed with DNA for AIF binding with a similar efficiency, thus corroborating a computer-calculated molecular model in which the binding site within AIF is the same for distinct nucleic acid species, without a clear sequence specificity. Among the preferred electron donors and acceptors of AIF, nicotine adenine dinucleotide phosphate (NADP) was particularly efficient in enhancing the generation of higher-order AIF/DNA and AIF/RNA complexes. Altogether, these data support a model in which a direct interaction of AIF contributes to the compaction of nucleic acids within apoptotic cells.

Novel Scoring Functions Comprising QXP, SASA, and Protein Side-Chain Entropy Terms

Novel scoring functions that predict the affinity of a ligand for its receptor have been developed. They were built with several statistical tools (partial least squares, genetic algorithms, neural networks) and trained on a data set of 100 crystal structures of receptor-ligand complexes, with affinities spanning 10 log units. The new scoring functions contain both descriptors generated by the QXP docking program and new descriptors that were developed in-house. These new descriptors are based on solvent accessible surface areas and account for conformational entropy changes and desolvation effects of both ligand and receptor upon binding. The predictive r(2) values for a test set of 24 complexes are in the 0.712-0.741 range and RMS prediction errors in the 1.09-1.16 log K(d) range. Inclusion of the new descriptors led to significant improvements in affinity prediction, compared to scoring functions based on QXP descriptors alone. However, the QXP descriptors by themselves perform better in binding mode prediction. The performance of the linear models is comparable to that of the neural networks. The new functions perform very well, but they still need to be validated as universal tools for the prediction of binding affinity.

Engineering Protein Therapeutics: Predictive Performances of a Structure-Based Virtual Affinity Maturation Protocol

The implementation of a structure based virtual affinity maturation protocol and evaluation of its predictivity are presented. The in silico protocol is based on conformational sampling of the interface residues (using the Dead End Elimination/A* algorithm), followed by the estimation of the change of free energy of binding due to a point mutation, applying MM/PBSA calculations. Several implementations of the protocol have been evaluated for 173 mutations in 7 different protein complexes for which experimental data were available: the use of the Boltzamnn averaged predictor based on the free energy of binding (ΔΔG(*)) combined with the one based on its polar component only (ΔΔE(pol*)) led to the proposal of a subset of mutations out of which 45% would have successfully enhanced the binding. When focusing on those mutations that are less likely to be introduced by natural in vivo maturation methods (99 mutations with at least two base changes in the codon), the success rate is increased to 63%. In another evaluation, focusing on 56 alanine scanning mutations, the in silico protocol was able to detect 89% of the hot-spots.