A Multiparametric Pharmacogenomic Strategy for Drug Repositioning predicts Therapeutic Efficacy for Glioblastoma Cell Lines

Background Poor prognosis of glioblastoma patients and the extensive heterogeneity of glioblastoma at both the molecular and cellular level necessitates developing novel individualized treatment modalities via genomics-driven approaches. Methods This study leverages numerous pharmacogenomic and tissue databases to examine drug repositioning for glioblastoma. RNAseq of glioblastoma tumor samples from The Cancer Genome Atlas (TCGA, n=117) were compared to “normal” frontal lobe samples from Genotype-Tissue Expression Portal (GTEX, n=120) to find differentially expressed genes (DEGs). Using compound-gene expression data and drug activity data from the Library of Integrated Network-Based Cellular Signatures (LINCS, n=66,512 compounds) CCLE (71 glioma cell lines), and Chemical European Molecular Biology Laboratory (ChEMBL) platforms, we employed a summarized reversal gene expression metric (sRGES) to “reverse” the resultant disease signature for GBM and its subtypes. A multi-parametric strategy was employed to stratify compounds capable of blood brain barrier penetrance with a favorable pharmacokinetic profile (CNS-MPO). Results Significant correlations were identified between sRGES and drug efficacy in GBM cell lines in both ChEMBL(r=0.37,p<.001) and Cancer Therapeutic Response Portal (CTRP) databases (r=0.35, p<0.001). Our multiparametric algorithm identified two classes of drugs with highest sRGES and CNS-MPO: HDAC inhibitors (vorinostat and entinostat) and topoisomerase inhibitors suitable for drug repurposing. Conclusions Our studies suggest that reversal of glioblastoma disease signature correlates with drug potency for various GBM subtypes. This multiparametric approach may set the foundation for an early-phase personalized -omics clinical trial for glioblastoma by effectively identifying drugs that are capable of reversing the disease signature and have favorable pharmacokinetic and safety profiles.

Overview on Chikungunya Virus Infection: From Epidemiology to State-of-the-Art Experimental Models

Chikungunya virus (CHIKV) is currently one of the most relevant arboviruses to public health. It is a member of the Togaviridae family and alphavirus genus and causes an arthritogenic disease known as chikungunya fever (CHIKF). It is characterized by a multifaceted disease, which is distinguished from other arbovirus infections by the intense and debilitating arthralgia that can last for months or years in some individuals. Despite the great social and economic burden caused by CHIKV infection, there is no vaccine or specific antiviral drugs currently available. Recent outbreaks have shown a change in the severity profile of the disease in which atypical and severe manifestation lead to hundreds of deaths, reinforcing the necessity to understand the replication and pathogenesis processes. CHIKF is a complex disease resultant from the infection of a plethora of cell types. Although there are several in vivo models for studying CHIKV infection, none of them reproduces integrally the disease signature observed in humans, which is a challenge for vaccine and drug development. Therefore, understanding the potentials and limitations of the state-of-the-art experimental models is imperative to advance in the field. In this context, the present review outlines the present knowledge on CHIKV epidemiology, replication, pathogenesis, and immunity and also brings a critical perspective on the current in vitro and in vivo state-of-the-art experimental models of CHIKF.

Computational drug repurposing against SARS-CoV-2 reveals plasma membrane cholesterol depletion as key factor of antiviral drug activity

Comparing SARS-CoV-2 infection-induced gene expression signatures to drug treatment-induced gene expression signatures is a promising bioinformatic tool to repurpose existing drugs against SARS-CoV-2. The general hypothesis of signature based drug repurposing is that drugs with inverse similarity to a disease signature can reverse disease phenotype and thus be effective against it. However, in the case of viral infection diseases, like SARS-CoV-2, infected cells also activate adaptive, antiviral pathways, so that the relationship between effective drug and disease signature can be more ambiguous.To address this question, we analysed gene expression data from in vitro SARS-CoV-2 infected cell lines, and gene expression signatures of drugs showing anti-SARS-CoV-2 activity. Our extensive functional genomic analysis showed that both infection and treatment with in vitro effective drugs leads to activation of antiviral pathways like NFkB and JAK-STAT. Based on the similarity - and not inverse similarity - between drug and infection-induced gene expression signatures, we were able to predict the in vitro antiviral activity of drugs. We also identified SREBF1/2, key regulators of lipid metabolising enzymes, as the most activated transcription factors by several in vitro effective antiviral drugs. Using a fluorescently labeled cholesterol sensor, we showed that these drugs decrease the cholesterol levels of plasma-membrane. Supplementing drug-treated cells with cholesterol reversed the in vitro antiviral effect, suggesting the depleting plasma-membrane cholesterol plays a key role in virus inhibitory mechanism.Our results can help to more effectively repurpose approved drugs against SARS-CoV-2, and also highlights key mechanisms behind their antiviral effect.Abstract Figure

Pathophysiological Effects of Overactive STIM1 on Murine Muscle Function and Structure

Store-operated Ca2+ entry (SOCE) is a ubiquitous mechanism regulating extracellular Ca2+ entry to control a multitude of Ca2+-dependent signaling pathways and cellular processes. SOCE relies on the concerted activity of the reticular Ca2+ sensor STIM1 and the plasma membrane Ca2+ channel ORAI1, and dysfunctions of these key factors result in human pathologies. STIM1 and ORAI1 gain-of-function (GoF) mutations induce excessive Ca2+ influx through SOCE over-activation, and cause tubular aggregate myopathy (TAM) and Stormorken syndrome (STRMK), two overlapping disorders characterized by muscle weakness, and additional multi-systemic signs affecting growth, platelets, spleen, skin, and intellectual abilities. In order to investigate the pathophysiological effect of overactive SOCE on muscle function and structure, we combined transcriptomics with morphological and functional studies on a TAM/STRMK mouse model. Muscles from Stim1R304W/+ mice displayed aberrant expression profiles of genes implicated in Ca2+ handling and excitation-contraction coupling (ECC), and in vivo investigations evidenced delayed muscle contraction and relaxation kinetics. We also identified signs of reticular stress and abnormal mitochondrial activity, and histological and respirometric analyses on muscle samples revealed enhanced myofiber degeneration associated with reduced mitochondrial respiration. Taken together, we uncovered a molecular disease signature and deciphered the pathomechanism underlying the functional and structural muscle anomalies characterizing TAM/STRMK.

Topographic divergence of atypical cortical asymmetry and regional atrophy patterns in temporal lobe epilepsy: a worldwide enigma study

Temporal lobe epilepsy (TLE), a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter pathology in TLE relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multi-site ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 TLE patients and 1,418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in TLE, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity, and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of TLE-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of TLE and may inform future discovery and validation of complementary MRI biomarkers in TLE.

Urinary Extracellular Vesicle Protein Profiles Discriminate Different Clinical Subgroups of Children with Idiopathic Nephrotic Syndrome

Idiopathic nephrotic syndrome (INS) is the most frequent primary glomerular disease in children, displaying high grade proteinuria and oedema. The mainstay of therapy are steroids, and patients are usually classified according to the treatment response (sensitive vs. resistant). The mechanisms involved in INS pathogenesis and treatment responsiveness have not yet been identified. In this context, the analysis of urinary extracellular vesicles (UEv) is interesting, since they represent a molecular snapshot of the parental cells, offering a “fingerprint” for monitoring their status. Therefore, the aim of this study is to verify the feasibility of using UEv of INS patients as indicators of therapy response and its prediction. UEv were isolated from the urine of pediatric patients in remission after therapy; they showed characteristic electrophoresis profiles that matched specific patient subgroups. We then built a statistical model to interpret objectively each patient UEv protein profile: in particular, steroid-resistant patients cluster together with a very distinct pattern from other INS patients and controls. In conclusion, the evaluation of the UEv protein profile looks promising in the investigation of INS, showing a disease signature that might predict clinical evolution.