Prioritised identification of structural classes of natural products from higher plants in the expedition of antimalarial drug discovery

The emergence and spread of drug-recalcitrant Plasmodium falciparum parasites threaten to reverse the gains made in the fight against malaria. Urgent measures need to be taken to curb this impending challenge. The higher plant-derived sesquiterpene, quinoline alkaloids, and naphthoquinone natural product classes of compounds have previously served as phenomenal chemical scaffolds from which integral antimalarial drugs were developed. Historical successes serve as an inspiration for the continued investigation of plant-derived natural products compounds in search of novel molecular templates from which new antimalarial drugs could be developed. The aim of this study was to identify potential chemical scaffolds for malaria drug discovery following analysis of historical data on phytochemicals screened in vitro against P. falciparum. To identify these novel scaffolds, we queried an in-house manually curated database of plant-derived natural product compounds and their in vitro biological data. Natural products were assigned to different structural classes using NPClassifier. To identify the most promising chemical scaffolds, we then correlated natural compound class with bioactivity and other data, namely (i) potency, (ii) resistance index, (iii) selectivity index and (iv) physicochemical properties. We used an unbiased scoring system to rank the different natural product classes based on the assessment of their bioactivity data. From this analysis we identified the top-ranked natural product pathway as the alkaloids. The top three ranked super classes identified were (i) pseudoalkaloids, (ii) naphthalenes and (iii) tyrosine alkaloids and the top five ranked classes (i) quassinoids (of super class triterpenoids), (ii) steroidal alkaloids (of super class pseudoalkaloids) (iii) cycloeudesmane sesquiterpenoids (of super class triterpenoids) (iv) isoquinoline alkaloids (of super class tyrosine alkaloids) and (v) naphthoquinones (of super class naphthalenes). Launched chemical space of these identified classes of compounds was, by and large, distinct from that of 'legacy' antimalarial drugs. Our study was able to identify chemical scaffolds with acceptable biological properties that are structurally different from current and previously used antimalarial drugs. These molecules have the potential to be developed into new antimalarial drugs.

Leveraging off higher plant phylogenetic insights for antiplasmodial drug discovery

The antimalarial drug-resistance conundrum which threatens to reverse the great strides taken to curb the malaria scourge warrants an urgent need to find novel chemical scaffolds to serve as templates for the development of new antimalarial drugs. Plants represent a viable alternative source for the discovery of unique potential antiplasmodial chemical scaffolds. To expedite the discovery of new antiplasmodial compounds from plants, the aim of this study was to use phylogenetic analysis to identify higher plant orders and families that can be rationally prioritised for antimalarial drug discovery. We queried the PubMed database for publications documenting antiplasmodial properties of natural compounds isolated from higher plants. Thereafter, we manually collated compounds reported along with plant species of origin and relevant pharmacological data. We systematically assigned antiplasmodial-associated plant species into recognised families and orders, and then computed the resistance index, selectivity index and physicochemical properties of the compounds from each taxonomic group. Correlating the generated phylogenetic trees and the biological data of each clade allowed for the identification of 3 'hot' plant orders and families. The top 3 ranked plant orders were the (i) Caryophyllales, (ii) Buxales, and (iii) Chloranthales. The top 3 ranked plant families were the (i) Ancistrocladaceae, (ii) Simaroubaceae, and (iii) Buxaceae. The highly active natural compounds (IC50 ≤ 1 µM) isolated from these plant orders and families are structurally unique to the 'legacy' antimalarial drugs. Our study was able to identify the most prolific taxa at order and family rank that we propose be prioritised in the search for potent, safe and drug-like antimalarial molecules.

Extraction of phytochemicals from the pomegranate (Punica granatum L., Punicaceae) by reverse iontophoresis

Plant metabolic profiling can provide a wealth of information regarding the biochemical status of the organism, but sample acquisition typically requires an invasive and/or destructive extraction process. Reverse iontophoresis (RI) imposes a small electric field across a biological membrane to substantially enhance the transport of charged and polar compounds and has been employed, in particular, to extract biomarkers of interest across human skin. The objective of this work was to examine the capability of RI to sample phytochemicals in a minimally invasive fashion in fructo (i.e., from the intact fruit). RI was principally used to extract a model, bioactive compound - specifically, ellagic acid - from the fruit peel of Punica granatum L. The RI sampling protocol was refined using isolated peel, and a number of experimental factors were examined and optimised, including preparation of the peel samples, the current intensity applied and the pH of the medium into which samples were collected. The most favourable conditions (3 mA current for a period of 1 hour, into a buffer at pH 7.4) were then applied to the successful RI extraction of ellagic acid from intact pomegranates. Multiple additional phytochemicals were also extracted and identified by liquid chromatography with tandem mass spectrometry (LC-MS/MS). A successful proof-of-concept has been achieved, demonstrating the capability to non-destructively extract phytochemicals of interest from intact fruit.

Deep Learning-driven research for drug discovery: Tackling Malaria

Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.

Analysis of SOX2-Regulated Transcriptome in Glioma Stem Cells

Introduction

Glioblastoma is the most malignant brain tumor in adults and is associated with poor survival despite multimodal treatments. Glioma stem-like cells (GSCs) are cells functionally defined by their self-renewal potential and the ability to reconstitute the original tumor upon orthotopic implantation. They have been postulated to be the culprit of glioma chemo- and radio-resistance ultimately leading to relapse. Understanding the molecular circuits governing the GSC compartment is essential. SOX2, a critical transcription regulator of embryonic and neural stem cell function, is deregulated in GSCs however; the precise molecular pathways regulated by this gene in GSCs remain poorly understood.

Results

We performed a genome-wide analysis of SOX2-regulated transcripts in GSCs, using a microarray. We identified a total of 2048 differentially expressed coding transcripts and 261 non-coding transcripts. Cell adhesion and cell-cell signaling are among the most enriched terms using Gene Ontology (GO) classification. The pathways altered after SOX2 down-modulation includes multiple cellular processes such as amino-acid metabolism and intercellular signaling cascades. We also defined and classified the set of non-coding transcripts differentially expressed regulated by SOX2 in GSCs, and validated two of them.

Conclusions

We present a comprehensive analysis of the transcriptome controlled by SOX2 in GSCs, gaining insights in the understanding of the potential roles of SOX2 in glioblastoma.

Multicentric Genome-Wide Association Study for Primary Spontaneous Pneumothorax

Despite elevated incidence and recurrence rates for Primary Spontaneous Pneumothorax (PSP), little is known about its etiology, and the genetics of idiopathic PSP remains unexplored. To identify genetic variants contributing to sporadic PSP risk, we conducted the first PSP genome-wide association study. Two replicate pools of 92 Portuguese PSP cases and of 129 age- and sex-matched controls were allelotyped in triplicate on the Affymetrix Human SNP Array 6.0 arrays. Markers passing quality control were ranked by relative allele score difference between cases and controls (|RAS_diff|), by a novel cluster method and by a combined Z-test. 101 single nucleotide polymorphisms (SNPs) were selected using these three approaches for technical validation by individual genotyping in the discovery dataset. 87 out of 94 successfully tested SNPs were nominally associated in the discovery dataset. Replication of the 87 technically validated SNPs was then carried out in an independent replication dataset of 100 Portuguese cases and 425 controls. The intergenic rs4733649 SNP in chromosome 8 (between LINC00824 and LINC00977) was associated with PSP in the discovery (P = 4.07E-03, OR_C[95% CI] = 1.88[1.22–2.89]), replication (P = 1.50E-02, OR_C[95% CI] = 1.50[1.08–2.09]) and combined datasets (P = 8.61E-05, OR_C[95% CI] = 1.65[1.29–2.13]). This study identified for the first time one genetic risk factor for sporadic PSP, but future studies are warranted to further confirm this finding in other populations and uncover its functional role in PSP pathogenesis.