Structural dynamics of Na+ and Ca2+ interactions with full-size mammalian NCX.

Cytosolic Ca2+ and Na+ allosterically regulate Na+/Ca2+ exchanger (NCX) proteins to vary the NCX-mediated Ca2+ entry/exit rates in diverse cell types. To resolve the structure-based dynamic mechanisms underlying the ion-dependent allosteric regulation in mammalian NCXs, we analyze the apo, Ca2+, and Na+-bound species of the brain NCX1.4 variant using hydrogen-deuterium exchange mass spectrometry (HDX-MS) and molecular dynamics (MD) simulations. Ca2+ binding to the cytosolic regulatory domains (CBD1 and CBD2) rigidifies the intracellular regulatory loop (5L6) and promotes its interaction with the membrane domains. Either Na+ or Ca2+ stabilizes the intracellular portions of transmembrane helices TM3, TM4, TM9, TM10, and their connecting loops (3L4 and 9L10), thereby exposing previously unappreciated regulatory sites. Ca2+ or Na+ also rigidifies the palmitoylation domain (TMH2), and neighboring TM1/TM6 bundle, thereby uncovering a structural entity for modulating the ion transport rates. The present analysis provides new structure-dynamic clues underlying the regulatory diversity among tissue-specific NCX variants.

Correction: Erlichman et al. Tumor Cell-Autonomous Pro-Metastatic Activities of PD-L1 in Human Breast Cancer Are Mediated by PD-L1-S283 and Chemokine Axes. Cancers 2022, 14, 1042.

The authors apologize for several typos/technical inaccuracies in the original article [...].

Tumor Cell-Autonomous Pro-Metastatic Activities of PD-L1 in Human Breast Cancer Are Mediated by PD-L1-S283 and Chemokine Axes.

Therapies targeting the PD-L1/PD-1 axis have recently been introduced to triple-negative breast cancer (TNBC) with limited efficacy, suggesting that this axis promotes tumor progression through mechanisms other than immune suppression. Here, we over-expressed WT-PD-L1 in human TNBC cells (express endogenous PD-L1) and in luminal-A breast cancer cells (no endogenous PD-L1 expression) and demonstrated that cell-autonomous PD-L1 activities lead to increased tumor cell growth, invasion and release of pro-metastatic factors (CXCL8, sICAM-1, GM-CSF). These activities were promoted by PD-1 and were inhibited by mutating S283 in PD-L1. Invasion of WT-PD-L1-cells required signaling by chemokine receptors CXCR1/2, CCR2 and CCR5 through autocrine circuits involving CXCL8, CCL2 and CCL5. Studies with T cell-deficient mice demonstrated that cell-autonomous WT-PD-L1 activities in TNBC cells increased tumor growth and metastasis compared to knock-out (KO)-PD-L1-cells, whereas S283A-PD-L1-expressing cells had minimal ability to form tumors and did not metastasize. Overall, our findings reveal autonomous and PD-1-induced tumor-promoting activities of PD-L1 that depend on S283 and on chemokine circuits. These results suggest that TNBC patients whose tumors express PD-L1 could benefit from therapies that prevent immune suppression by targeting PD-1/CTLA-4, alongside with antibodies to PD-L1, which would allow maximal impact by mainly targeting the cancer cells.

Alleviation of a polyglucosan storage disorder by enhancement of autophagic glycogen catabolism.

This work employs adult polyglucosan body disease (APBD) models to explore the efficacy and mechanism of action of the polyglucosan-reducing compound 144DG11. APBD is a glycogen storage disorder (GSD) caused by glycogen branching enzyme (GBE) deficiency causing accumulation of poorly branched glycogen inclusions called polyglucosans. 144DG11 improved survival and motor parameters in a GBE knockin (Gbeys/ys ) APBD mouse model. 144DG11 reduced polyglucosan and glycogen in brain, liver, heart, and peripheral nerve. Indirect calorimetry experiments revealed that 144DG11 increases carbohydrate burn at the expense of fat burn, suggesting metabolic mobilization of pathogenic polyglucosan. At the cellular level, 144DG11 increased glycolytic, mitochondrial, and total ATP production. The molecular target of 144DG11 is the lysosomal membrane protein LAMP1, whose interaction with the compound, similar to LAMP1 knockdown, enhanced autolysosomal degradation of glycogen and lysosomal acidification. 144DG11 also enhanced mitochondrial activity and modulated lysosomal features as revealed by bioenergetic, image-based phenotyping and proteomics analyses. As an effective lysosomal targeting therapy in a GSD model, 144DG11 could be developed into a safe and efficacious glycogen and lysosomal storage disease therapy.

A novel specific PERK activator reduces toxicity and extends survival in Huntington's disease models.

One of the pathways of the unfolded protein response, initiated by PKR-like endoplasmic reticulum kinase (PERK), is key to neuronal homeostasis in neurodegenerative diseases. PERK pathway activation is usually accomplished by inhibiting eIF2α-P dephosphorylation, after its phosphorylation by PERK. Less tried is an approach involving direct PERK activation without compromising long-term recovery of eIF2α function by dephosphorylation. Here we show major improvement in cellular (STHdhQ111/111) and mouse (R6/2) Huntington's disease (HD) models using a potent small molecule PERK activator that we developed, MK-28. MK-28 showed PERK selectivity in vitro on a 391-kinase panel and rescued cells (but not PERK-/- cells) from ER stress-induced apoptosis. Cells were also rescued by the commercial PERK activator CCT020312 but MK-28 was significantly more potent. Computational docking suggested MK-28 interaction with the PERK activation loop. MK-28 exhibited remarkable pharmacokinetic properties and high BBB penetration in mice. Transient subcutaneous delivery of MK-28 significantly improved motor and executive functions and delayed death onset in R6/2 mice, showing no toxicity. Therefore, PERK activation can treat a most aggressive HD model, suggesting a possible approach for HD therapy and worth exploring for other neurodegenerative disorders.

Computational design of substrate selective inhibition.

Most enzymes act on more than a single substrate. There is frequently a need to block the production of a single pathogenic outcome of enzymatic activity on a substrate but to avoid blocking others of its catalytic actions. Full blocking might cause severe side effects because some products of that catalysis may be vital. Substrate selectivity is required but not possible to achieve by blocking the catalytic residues of an enzyme. That is the basis of the need for "Substrate Selective Inhibitors" (SSI), and there are several molecules characterized as SSI. However, none have yet been designed or discovered by computational methods. We demonstrate a computational approach to the discovery of Substrate Selective Inhibitors for one enzyme, Prolyl Oligopeptidase (POP) (E.C 3.4.21.26), a serine protease which cleaves small peptides between Pro and other amino acids. Among those are Thyrotropin Releasing Hormone (TRH) and Angiotensin-III (Ang-III), differing in both their binding (Km) and in turnover (kcat). We used our in-house "Iterative Stochastic Elimination" (ISE) algorithm and the structure-based "Pharmacophore" approach to construct two models for identifying SSI of POP. A dataset of ~1.8 million commercially available molecules was initially reduced to less than 12,000 which were screened by these models to a final set of 20 molecules which were sent for experimental validation (five random molecules were tested for comparison). Two molecules out of these 20, one with a high score in the ISE model, the other successful in the pharmacophore model, were confirmed by in vitro measurements. One is a competitive inhibitor of Ang-III (increases its Km), but non-competitive towards TRH (decreases its Vmax).

Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling.

PPAR-δ agonists are known to enhance fatty acid metabolism, preserving glucose and physical endurance and are suggested as candidates for treating metabolic diseases. None have reached the clinic yet. Our Machine Learning algorithm called "Iterative Stochastic Elimination" (ISE) was applied to construct a ligand-based multi-filter ranking model to distinguish between confirmed PPAR-δ agonists and random molecules. Virtual screening of 1.56 million molecules by this model picked ~2500 top ranking molecules. Subsequent docking to PPAR-δ structures was mainly evaluated by geometric analysis of the docking poses rather than by energy criteria, leading to a set of 306 molecules that were sent for testing in vitro. Out of those, 13 molecules were found as potential PPAR-δ agonist leads with EC50 between 4-19 nM and 14 others with EC50 below 10 µM. Most of the nanomolar agonists were found to be highly selective for PPAR-δ and are structurally different than agonists used for model building.