THz absorption spectrum of the CO2–H2O complex: Observation and assignment of intermolecular van der Waals vibrations

Terahertz absorption spectra have been recorded for the weakly bound CO2-H2O complex embedded in cryogenic neon matrices at 2.8 K. The three high-frequency van der Waals vibrational transitions associated with out-of-plane wagging, in-plane rocking, and torsional motion of the isotopic H2O subunit have been assigned and provide crucial observables for benchmark theoretical descriptions of this systems' flat intermolecular potential energy surface. A (semi)-empirical value for the zero-point energy of 273 ± 15 cm(-1) from the class of intermolecular van der Waals vibrations is proposed and the combination with high-level quantum chemical calculations provides a value of 726 ± 15 cm(-1) for the dissociation energy D0

Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data

Understanding the mechanisms of cell function and drug action is a major endeavor in the pharmaceutical industry. Drug effects are governed by the intrinsic properties of the drug (i.e., selectivity and potency) and the specific signaling transduction network of the host (i.e., normal vs. diseased cells). Here, we describe an unbiased, phosphoproteomicbased approach to identify drug effects by monitoring drug-induced topology alterations. With the proposed method, drug effects are investigated under several conditions on a cell-type specific signaling network. First, starting with a generic pathway made of logical gates, we build a cell-type specific map by constraining it to fit 13 key phopshoprotein signals under 55 experimental cases. Fitting is performed via a formulation as an Integer Linear Program (ILP) and solution by standard ILP solvers; a procedure that drastically outperforms previous fitting schemes. Then, knowing the cell topology, we monitor the same key phopshoprotein signals under the presence of drug and cytokines and we re-optimize the specific map to reveal the drug-induced topology alterations. To prove our case, we make a pathway map for the hepatocytic cell line HepG2 and we evaluate the effects of 4 drugs: 3 selective inhibitors for the Epidermal Growth Factor Receptor (EGFR) and a non selective drug. We confirm effects easily predictable from the drugs’ main target (i.e. EGFR inhibitors blocks the EGFR pathway) but we also uncover unanticipated effects due to either drug promiscuity or the cell’s specific topology. An interesting finding is that the selective EGFR inhibitor Gefitinib is able to inhibit signaling downstream the Interleukin-1alpha (IL-1α) pathway; an effect that cannot be extracted from binding affinity based approaches. Our method represents an unbiased approach to identify drug effects on a small to medium size pathways and is scalable to larger topologies with any type of signaling perturbations (small molecules, 3 RNAi etc). The method is a step towards a better picture of drug effects in pathways, the cornerstone in identifying the mechanisms of drug efficacy and toxicity

NF1 regulates mesenchymal gliblastoma plasticity and aggressiveness through the AP-1 transcription factor FOSL1

The molecular basis underlying glioblastoma (GBM) heterogeneity and plasticity is not fully understood. Using transcriptomic data of human patient-derived brain tumor stem cell lines (BTSCs), classified based on GBM-intrinsic signatures, we identify the AP-1 transcription factor FOSL1 as a key regulator of the mesenchymal (MES) subtype. We provide a mechanistic basis to the role of the neurofibromatosis type 1 gene (NF1), a negative regulator of the RAS/MAPK pathway, in GBM mesenchymal transformation through the modulation of FOSL1 expression. Depletion of FOSL1 in NF1-mutant human BTSCs and Kras-mutant mouse neural stem cells results in loss of the mesenchymal gene signature and reduction in stem cell properties and in vivo tumorigenic potential. Our data demonstrate that FOSL1 controls GBM plasticity and aggressiveness in response to NF1 alterations

Monotherapy efficacy of blood-brain barrier permeable small molecule reactivators of protein phosphatase 2A in glioblastoma

Glioblastoma is a fatal disease in which most targeted therapies have clinically failed. However, pharmacological reactivation of tumour suppressors has not been thoroughly studied as yet as a glioblastoma therapeutic strategy. Tumour suppressor protein phosphatase 2A is inhibited by non-genetic mechanisms in glioblastoma, and thus, it would be potentially amendable for therapeutic reactivation. Here, we demonstrate that small molecule activators of protein phosphatase 2A, NZ-8-061 and DBK-1154, effectively cross the in vitro model of blood-brain barrier, and in vivo partition to mouse brain tissue after oral dosing. In vitro, small molecule activators of protein phosphatase 2A exhibit robust cell-killing activity against five established glioblastoma cell lines, and nine patient-derived primary glioma cell lines. Collectively, these cell lines have heterogeneous genetic background, kinase inhibitor resistance profile and stemness properties; and they represent different clinical glioblastoma subtypes. Moreover, small molecule activators of protein phosphatase 2A were found to be superior to a range of kinase inhibitors in their capacity to kill patient-derived primary glioma cells. Oral dosing of either of the small molecule activators of protein phosphatase 2A significantly reduced growth of infiltrative intracranial glioblastoma tumours. DBK-1154, with both higher degree of brain/blood distribution, and more potent in vitro activity against all tested glioblastoma cell lines, also significantly increased survival of mice bearing orthotopic glioblastoma xenografts. In summary, this report presents a proof-of-principle data for blood-brain barrier-permeable tumour suppressor reactivation therapy for glioblastoma cells of heterogenous molecular background. These results also provide the first indications that protein phosphatase 2A reactivation might be able to challenge the current paradigm in glioblastoma therapies which has been strongly focused on targeting specific genetically altered cancer drivers with highly specific inhibitors. Based on demonstrated role for protein phosphatase 2A inhibition in glioblastoma cell drug resistance, small molecule activators of protein phosphatase 2A may prove to be beneficial in future glioblastoma combination therapies.Peer reviewe

A Patient-Derived Cell Atlas Informs Precision Targeting of Glioblastoma

Glioblastoma (GBM) is a malignant brain tumor with few therapeutic options. The disease presents with a complex spectrum of genomic aberrations, but the pharmacological consequences of these aberrations are partly unknown. Here, we report an integrated pharmacogenomic analysis of 100 patient-derived GBM cell cultures from the human glioma cell culture (HGCC) cohort. Exploring 1,544 drugs, we find that GBM has two main pharmacological subgroups, marked by differential response to proteasome inhibitors and mutually exclusive aberrations in TP53 and CDKN2A/B. We confirm this trend in cell and in xenotransplantation models, and identify both Bcl-2 family inhibitors and p53 activators as potentiators of proteasome inhibitors in GBM cells, We can further predict the responses of individual cell cultures to several existing drug classes, presenting opportunities for drug repurposing and design of stratified trials. Our functionally profiled biobank provides a valuable resource for the discovery of new treatments for GBM.Patrik Johansson, Cecilia Krona and Soumi Kundu share first authorship</p

Preclinical quality, safety, and efficacy of a human embryonic stem cell-derived product for the treatment of Parkinson’s disease, STEM-PD

Cell replacement therapies for Parkinson’s disease (PD) based on transplantation of pluripotent stem cell-derived dopaminergic neurons are now entering clinical trials. Here, we present quality, safety, and efficacy data supporting the first-in-human STEM-PD phase I/IIa clinical trial along with the trial design. The STEM-PD product was manufactured under GMP and quality tested in vitro and in vivo to meet regulatory requirements. Importantly, no adverse effects were observed upon testing of the product in a 39-week rat GLP safety study for toxicity, tumorigenicity, and biodistribution, and a non-GLP efficacy study confirmed that the transplanted cells mediated full functional recovery in a pre-clinical rat model of PD. We further observed highly comparable efficacy results between two different GMP batches, verifying that the product can be serially manufactured. A fully in vivo-tested batch of STEM-PD is now being used in a clinical trial of 8 patients with moderate PD, initiated in 2022

Use of Data-Biased Random Walks on Graphs for the Retrieval of Context-Specific Networks from Genomic Data

Extracting network-based functional relationships within genomic datasets is an important challenge in the computational analysis of large-scale data. Although many methods, both public and commercial, have been developed, the problem of identifying networks of interactions that are most relevant to the given input data still remains an open issue. Here, we have leveraged the method of random walks on graphs as a powerful platform for scoring network components based on simultaneous assessment of the experimental data as well as local network connectivity. Using this method, NetWalk, we can calculate distribution of Edge Flux values associated with each interaction in the network, which reflects the relevance of interactions based on the experimental data. We show that network-based analyses of genomic data are simpler and more accurate using NetWalk than with some of the currently employed methods. We also present NetWalk analysis of microarray gene expression data from MCF7 cells exposed to different doses of doxorubicin, which reveals a switch-like pattern in the p53 regulated network in cell cycle arrest and apoptosis. Our analyses demonstrate the use of NetWalk as a valuable tool in generating high-confidence hypotheses from high-content genomic data

Deterministic Effects Propagation Networks for reconstructing protein signaling networks from multiple interventions

<p>Abstract</p> <p>Background</p> <p>Modern gene perturbation techniques, like RNA interference (RNAi), enable us to study effects of targeted interventions in cells efficiently. In combination with mRNA or protein expression data this allows to gain insights into the behavior of complex biological systems.</p> <p>Results</p> <p>In this paper, we propose Deterministic Effects Propagation Networks (DEPNs) as a special Bayesian Network approach to reverse engineer signaling networks from a combination of protein expression and perturbation data. DEPNs allow to reconstruct protein networks based on combinatorial intervention effects, which are monitored via changes of the protein expression or activation over one or a few time points. Our implementation of DEPNs allows for latent network nodes (i.e. proteins without measurements) and has a built in mechanism to impute missing data. The robustness of our approach was tested on simulated data. We applied DEPNs to reconstruct the <it>ERBB </it>signaling network in <it>de novo </it>trastuzumab resistant human breast cancer cells, where protein expression was monitored on Reverse Phase Protein Arrays (RPPAs) after knockdown of network proteins using RNAi.</p> <p>Conclusion</p> <p>DEPNs offer a robust, efficient and simple approach to infer protein signaling networks from multiple interventions. The method as well as the data have been made part of the latest version of the R package "nem" available as a supplement to this paper and via the Bioconductor repository.</p