Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data

Celastrol: A century-long journey from the isolation to the biotechnological production and the development of an antiobesity drug

Celastrol, a triterpenoid found in the root of the traditional medicinal plant Tripterygium wilfordii, is a potent anti-inflammatory and antiobesity agent. However, pharmacological exploitation of celastrol has been hindered by the limited accessibility of plant material, the co-existence of other toxic compounds in the same plant tissue, and the lack of an efficient chemical synthesis method. In this review, we highlight recent progress in elucidating celastrol biosynthesis and discuss how this knowledge can facilitate its scalable bioproduction using cell factories and its further development as an antiobesity and anti-inflammatory drug.

Systematic computational strategies for identifying protein targets and lead discovery

Computational algorithms and tools have retrenched the drug discovery and development timeline. The applicability of computational approaches has gained immense relevance owing to the dramatic surge in the structural information of biomacromolecules and their heteromolecular complexes. Computational methods are now extensively used in identifying new protein targets, druggability assessment, pharmacophore mapping, molecular docking, the virtual screening of lead molecules, bioactivity prediction, molecular dynamics of protein-ligand complexes, affinity prediction, and for designing better ligands. Herein, we provide an overview of salient components of recently reported computational drug-discovery workflows that includes algorithms, tools, and databases for protein target identification and optimized ligand selection.

OGG1 as an Epigenetic Reader Affects NFκB: What This Means for Cancer

8-oxoguanine glycosylase 1 (OGG1), which was initially identified as the enzyme that catalyzes the first step in the DNA base excision repair pathway, is now also recognized as a modulator of gene expression. What is important for cancer is that OGG1 acts as a modulator of NFκB-driven gene expression. Specifically, oxidant stress in the cell transiently halts enzymatic activity of substrate-bound OGG1. The stalled OGG1 facilitates DNA binding of transactivators, such as NFκB to their cognate sites, enabling the expression of cytokines and chemokines, with ensuing recruitment of inflammatory cells. Recently, we highlighted chief aspects of OGG1 involvement in regulation of gene expression, which hold significance in lung cancer development. However, OGG1 has also been implicated in the molecular underpinning of acute myeloid leukemia. This review analyzes and discusses how these cells adapt through redox-modulated intricate connections, via interaction of OGG1 with NFκB, which provides malignant cells with alternative molecular pathways to transform their microenvironment, enabling adjustment, promoting cell proliferation, metastasis, and evading killing by therapeutic agents.

Emerging drugs targeting cellular redox homeostasis to eliminate acute myeloid leukemia stem cells

Acute myeloid leukemia (AML) is a very heterogeneous group of disorders with large differences in the percentage of immature blasts that presently are classified according to the specific mutations that trigger malignant proliferation among thousands of mutations reported thus far. It is an aggressive disease for which few targeted therapies are available and still has a high recurrence rate and low overall survival. The main reason for AML relapse is believed to be due to leukemic stem cells (LSCs) that have unlimited self-renewal capacity and long residence in a quiescent state, which promote greater resistance to traditional therapies for this cancer. AML LSCs have low oxidative stress levels, which appear to be caused by a combination of low mitochondrial activity and high activity of ROS-removing pathways. In this sense, oxidative stress has been thought to be an important new potential target for the treatment of AML patients, targeting the eradication of AML LSCs. The aim of this review is to discuss some drugs that induce oxidative stress to direct new goals for future research focusing on redox imbalance as an effective strategy to eliminate AML LSCs.

IRF8 and its related molecules as potential diagnostic biomarkers or therapeutic candidates and immune cell infiltration characteristics in steroid-induced osteonecrosis of the femoral head

PURPOSE

Steroid-induced osteonecrosis of the femoral head (SONFH) was a refractory orthopedic hip joint disease in the young and middle-aged people, but the pathogenesis of SONFH remained unclear. We aimed to identify the potential genes and screen potential therapeutic compounds for SONFH.

METHODS

The microarray was obtained for blood tissue from the GEO database, and then it identifies differentially expressed genes (DEGs). The DEGs were analyzed to obtain the differences in immune cell infiltration. The gene functional enrichment analysis of SONFH was analyzed. The PPI of DEGs was identified through the STRING database, and the cluster modules and hub genes were ascertained using MCODE and CytoHubba, and the ROC curve of hub genes was analyzed, and the tissues distribution of hub genes was understood by the HPA, Bgee and BioGPS databases. The hub genes and target miRNAs and corresponding upstream lncRNAs were predicted by TargetScan, miRDB and ENCORI database. Subsequently, we used CMap, DGIdb and L1000FWD databases to identify several potential therapeutic molecular compounds for SONFH. Finally, the AutoDockTools Vina, PyMOL and Discovery Studio were employed for molecular docking analyses between compounds and hub genes.

RESULTS

The microarray dataset GSE123568 was obtained related to SONFH. There were 372 DEGs including 197 upregulated genes and 175 downregulated genes by adjusted P value < 0.01 and |log₂FC|> 1. Several significant GSEA enrichment analysis and biological processes and KEGG pathway associated with SONFH were identified, which were significantly related to cytoskeleton organization, nucleobase-containing compound catabolic process, NOD-like receptor signaling pathway, MAPK signaling pathway, FoxO signaling pathway, neutrophil-mediated immunity, neutrophil degranulation and neutrophil activation involved in immune response. Activated T cells CD4 memory, B cells naïve, B cells memory, T cells CD8 and T cells gamma delta might be involved in the occurrence and development of SONFH. Three cluster modules were identified in the PPI network, and eleven hub genes including FPR2, LILRB2, MNDA, CCR1, IRF8, TYROBP, TLR1, HCK, TLR8, TLR2 and CCR2 were identified by Cytohubba, which were differed in bone marrow, adipose tissue and blood, and which had good diagnostic performance in SONFH. We identified IRF8 and 10 target miRNAs that was utilized including Targetsan, miRDB and ENCORI databases and 8 corresponding upstream lncRNAs that was revealed by ENCORI database. IRF8 was detected with consistent expression by qRT-PCR. Based on the CMap, DGIdb and L1000FWD databases, the 11 small molecular compounds that were most strongly therapeutic correlated with SONFH were estradiol, genistein, domperidone, lovastatin, myricetin, fenbufen, rosiglitazone, sirolimus, phenformin, vorinostat and vinblastine. All of 11 small molecules had good binding affinity with the IRF8 in molecular docking.

CONCLUSION

The occurrence of SONFH was associated with a "multi-target" and "multi-pathway" pattern, especially related to immunity, and IRF8 and its noncoding RNA were closely related to the development of SONFH. The CMap, DGIdb and L1000FWD databases could be effectively used in a systematic manner to predict potential drugs for the prevention and treatment of SONFH. However, additional clinical and experimental research is warranted.

Screening of Medications for Idiopathic Membranous Nephropathy Using Glomerular Whole-Genome Sequencing

Objective

To explore medications that have a therapeutic effect on idiopathic membranous nephropathy (IMN) using the Gene Expression Omnibus (GEO), the Connectivity Map (CMap) database, and bioinformatics approaches.

Methods

IMN patients' glomerular whole-genome sequencing data were retrieved and screened in the GEO database, differentially expressed genes were identified using GEO2R analysis, a PPI network was built in the STRING database, node degree values were calculated, and topological analysis was performed using the degree value to identify core genes. The WebGestalt database was used to perform GO enrichment and KEGG pathway analyses on the core genes. Candidate medications for the therapy of IMN were collected from the CMap database, and the candidate medications were then searched and analyzed.

Results

113 core genes were identified by topological analysis from the 1157 genes that were shown to be differentially expressed. The enrichment analysis identified several important gene functions and signaling pathways related to IMN. Some possible medications for the treatment of IMN have been found using the CMap database. Naringin, with the lowest CMap score, meaningful P value, and specificity score, was predicted as the most likely medication.

Conclusion

The GEO and CMap databases can be used to understand the molecular changes of IMN and to provide new ideas for medication research. However, medication candidates must undergo clinical and experimental testing.

Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing

Chemical-induced gene expression profiles provide critical information of chemicals in a biological system, thus offering new opportunities for drug discovery. Despite their success, large-scale analysis leveraging gene expressions is limited by time and cost. Although several methods for predicting gene expressions were proposed, they only focused on imputation and classification settings, which have limited applications to real-world scenarios of drug discovery. Therefore, a chemical-induced gene expression ranking (CIGER) framework is proposed to target a more realistic but more challenging setting in which overall rankings in gene expression profiles induced by de novo chemicals are predicted. The experimental results show that CIGER significantly outperforms existing methods in both ranking and classification metrics. Furthermore, a drug screening pipeline based on CIGER is proposed to identify potential treatments of drug-resistant pancreatic cancer. Our predictions have been validated by experiments, thereby showing the effectiveness of CIGER for phenotypic compound screening of precision medicine.

•

A new deep-learning method (CIGER) for chemical-induced gene expression ranking

•

CIGER can predict gene expression for de novo chemicals from chemical structures

•

We discovered drugs for the treatment of drug-resistant pancreatic cancer

In recent years, a phenotype-based drug discovery approach using chemical-induced gene expressions has shown to be effective in drug discovery and precision medicine. However, it is not feasible to experimentally determine chemical-induced gene expressions for all available chemicals of interest, thereby hindering the application of gene expression-based compound screening on a large scale. Thus, it is crucial to design a computational approach that can generate gene expression information for any chemicals. We proposed a new, deep-learning framework named chemical-induced gene expression ranking (CIGER) to predict a landmark gene expression profile (i.e., gene ranking) induced by de novo chemicals based on their chemical structures. Leveraging CIGER, we predicted and experimentally validated that several existing drugs can increase the therapeutic response on drug-resistant pancreatic cancer. Our results demonstrated the effectiveness of CIGER for precision drug discovery in practice.

Computational Bioactivity Fingerprint Similarities To Navigate the Discovery of Novel Scaffolds

As one of the central tasks of modern medicinal chemistry, scaffold hopping is expected to lead to the discovery of structural novel biological active compounds and broaden the chemical space of known active compounds. Here, we report the computational bioactivity fingerprint (CBFP) for easier scaffold hopping, where the predicted activities in multiple quantitative structure-activity relationship models are integrated to characterize the biological space of a molecule. In retrospective benchmarks, the CBFP representation shows outstanding scaffold hopping potential relative to other chemical descriptors. In the prospective validation for the discovery of novel inhibitors of poly [ADP-ribose] polymerase 1, 35 predicted compounds with diverse structures are tested, 25 of which show detectable growth-inhibitory activity; beyond this, the most potent (compound 6) has an IC₅₀ of 0.263 nM. These results support the use of CBFP representation as the bioactivity proxy of molecules to explore uncharted chemical space and discover novel compounds.

A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing

Phenotype-based compound screening has advantages over target-based drug discovery, but is unscalable and lacks understanding of mechanism. Chemical-induced gene expression profile provides a mechanistic signature of phenotypic response. However, the use of such data is limited by their sparseness, unreliability, and relatively low throughput. Few methods can perform phenotype-based de novo chemical compound screening. Here, we propose a mechanism-driven neural network-based method DeepCE, which utilizes graph neural network and multi-head attention mechanism to model chemical substructure-gene and gene-gene associations, for predicting the differential gene expression profile perturbed by de novo chemicals. Moreover, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves superior performances to state-of-the-art methods. The effectiveness of gene expression profiles generated from DeepCE is further supported by comparing them with observed data for downstream classification tasks. To demonstrate the value of DeepCE, we apply it to drug repurposing of COVID-19, and generate novel lead compounds consistent with clinical evidence. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data and screening novel chemicals for the modulation of a systemic response to disease.

Modulators of histone demethylase JMJD1C selectively target leukemic stem cells

Leukemic stem cells (LSCs) comprise a very rare cell population that results in the development of acute myeloid leukemia. The selective targeting of drivers in LSCs with small molecule inhibitors holds promise for treatment of acute myeloid leukemia. Recently, we reported the identification of inhibitors of the histone lysine demethylase JMJD1C that preferentially kill MLL rearranged acute leukemia cells. Here, we report the identification of jumonji domain modulator #7 (JDM‐7). Surface plasmon resonance analysis showed that JDM‐7 binds to JMJD1C and its family homolog JMJD1B. JDM‐7 did not significantly suppress cell proliferation in liquid cell culture at higher doses, although it led to a significant decrease in semi‐solid colony formation experiments at lower concentrations. Moreover, low doses of JDM‐7 did not suppress the proliferation of erythroid progenitor cells. We identified that JDM‐7 downregulates the LSC self‐renewal gene HOXA9 in leukemia cells. We further found that the structure of JDM‐7 is similar to that of tadalafil, a drug approved by the US Food and Drug Administration. Molecular docking and surface plasmon resonance analysis showed that tadalafil binds to JMJD1C. Moreover, similar to JDM‐7, tadalafil suppressed colony formation of leukemia cells in semi‐solid cell culture at a concentration that did not affect primary umbilical cord blood cells. In summary, we have identified JDM‐7 and tadalafil as potential JMJD1C modulators that selectively inhibit the growth of LSCs.

Underlying Mechanisms and Candidate Drugs for COVID-19 Based on the Connectivity Map Database

Background

The coronavirus disease 2019 (COVID-19) has become a worldwide public health crisis. At present, there are no effective antiviral drugs to treat COVID-19. Although some vaccines have been developed, late-stage clinical trials that allow licensure by regulatory agencies are still needed. Previous reports have indicated that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and SARS-CoV are highly homologous and both use angiotensin-converting enzyme 2 (ACE2) as the receptor to enter cells, and SARS-CoV infection reduces the ACE2 expression in the lung. Therefore, the analysis of genes co-expressed with ACE2 in the lung may uncover the underlying mechanism of COVID-19. Finally, we used the Connectivity map (Cmap) database to search for candidate drugs using transcriptome profiles of patients with COVID-19.

Method

Based on the differentially expressed genes (DEGs), indicated by the expression of RNAs isolated from bronchoalveolar lavage fluid (BALF) cells of patients with COVID-19, we performed functional enrichment analysis and hub gene cluster analysis. Furthermore, we identified genes co-expressed with ACE2 in healthy lung samples and analyzed the significant genes. Additionally, to identify several candidate drugs for the treatment of COVID-19, we queried Cmap using DEGs and genes co-expressed with ACE2.

Results and Conclusion

The up-regulated genes in the BALF cells of patients with COVID-19 are related to viral mRNA translation. The down-regulated genes are related to immune response functions. Genes positively correlated with ACE2 are related to immune defense and those negatively correlated are enriched in synaptic transmission functions. The results reflected prosperous viral proliferation and immune dysfunction in patients. Furthermore, ACE2 may not only mediate viral entrance, but also play an important role in immune defense. By using Cmap with transcriptome profiles of patients with COVID-19, we identified candidate drugs for the treatment of COVID-19, such as amantadine and acyclovir.

STAT3 transcription factor as target for anti-cancer therapy

STATs constitute a large family of transcription activators and transducers of signals that have an important role in many cell functions as regulation of proliferation and differentiation of the cell also regulation of apoptosis and angiogenesis. STAT3 as a member of that family, recently was discovered to have a vital role in progression of different types of cancers. The activation of STAT3 was observed to regulate multiple gene functions during cancer-like cell proliferation, differentiation, apoptosis, metastasis, inflammation, immunity, cell survival, and angiogenesis. The inhibition of STAT3 activation has been an important target for cancer therapy. Inhibitors of STAT3 have been used for a long time for treatment of many types of cancers like leukemia, melanoma, colon, and renal cancer. In this review article, we summarize and discuss different drugs inhibiting the action of STAT3 and used in treatment of different types of cancer.

A deep learning framework for high-throughput mechanism-driven phenotype compound screening

Target-based high-throughput compound screening dominates conventional one-drug-one-gene drug discovery process. However, the readout from the chemical modulation of a single protein is poorly correlated with phenotypic response of organism, leading to high failure rate in drug development. Chemical-induced gene expression profile provides an attractive solution to phenotype-based screening. However, the use of such data is currently limited by their sparseness, unreliability, and relatively low throughput. Several methods have been proposed to impute missing values for gene expression datasets. However, few existing methods can perform de novo chemical compound screening. In this study, we propose a mechanism-driven neural network-based method named DeepCE (Deep Chemical Expression) which utilizes graph convolutional neural network to learn chemical representation and multi-head attention mechanism to model chemical substructure-gene and gene-gene feature associations. In addition, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves the superior performances not only in de novo chemical setting but also in traditional imputation setting compared to state-of-the-art baselines for the prediction of chemical-induced gene expression. We further verify the effectiveness of gene expression profiles generated from DeepCE by comparing them with gene expression profiles in L1000 dataset for downstream classification tasks including drug-target and disease predictions. To demonstrate the value of DeepCE, we apply it to patient-specific drug repurposing of COVID-19 for the first time, and generate novel lead compounds consistent with clinical evidences. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data as well as screening novel chemicals for the modulation of systemic response to disease.

Fifteen-minute consultation: Pharmacogenomics: a guide for busy clinicians

The ultimate goal of pharmacogenomics is to understand how the effects of interindividual genetic variation impact on the response of patients to treatments and in turn optimise those treatments while minimising potential side effects. This review article provides a brief overview of pharmacogenomics, focusing on the methods used to understand how genes of interest are identified, how pharmacogenomics is currently used within paediatric clinical practice within the UK, potential areas for future use and some of the caveats specific to paediatric medicine.

Integrating pathway elucidation with yeast engineering to produce polpunonic acid the precursor of the anti-obesity agent celastrol

BACKGROUND

Celastrol is a promising anti-obesity agent that acts as a sensitizer of the protein hormone leptin. Despite its potent activity, a sustainable source of celastrol and celastrol derivatives for further pharmacological studies is lacking.

RESULTS

To elucidate the celastrol biosynthetic pathway and reconstruct it in Saccharomyces cerevisiae, we mined a root-transcriptome of Tripterygium wilfordii and identified four oxidosqualene cyclases and 49 cytochrome P450s as candidates to be involved in the early steps of celastrol biosynthesis. Using functional screening of the candidate genes in Nicotiana benthamiana, TwOSC4 was characterized as a novel oxidosqualene cyclase that produces friedelin, the presumed triterpenoid backbone of celastrol. In addition, three P450s (CYP712K1, CYP712K2, and CYP712K3) that act downstream of TwOSC4 were found to effectively oxidize friedelin and form the likely celastrol biosynthesis intermediates 29-hydroxy-friedelin and polpunonic acid. To facilitate production of friedelin, the yeast strain AM254 was constructed by deleting UBC7, which afforded a fivefold increase in friedelin titer. This platform was further expanded with CYP712K1 to produce polpunonic acid and a method for the facile extraction of products from the yeast culture medium, resulting in polpunonic acid titers of 1.4 mg/L.

CONCLUSION

Our study elucidates the early steps of celastrol biosynthesis and paves the way for future biotechnological production of this pharmacologically promising compound in engineered yeast strains.

Metabolomics Reveals that Cysteine Metabolism Plays a Role in Celastrol-Induced Mitochondrial Apoptosis in HL-60 and NB-4 Cells

Recently, celastrol has shown great potential for inducing apoptosis in acute myeloid leukemia cells, especially acute promyelocytic leukaemia cells. However, the mechanism is poorly understood. Metabolomics provides an overall understanding of metabolic mechanisms to illustrate celastrol's mechanism of action. We treated both nude mice bearing HL-60 cell xenografts in vivo and HL-60 cells as well as NB-4 cells in vitro with celastrol. Ultra-performance liquid chromatography coupled with mass spectrometry was used for metabolomics analysis of HL-60 cells in vivo and for targeted L-cysteine analysis in HL-60 and NB-4 cells in vitro. Flow cytometric analysis was performed to assess mitochondrial membrane potential, reactive oxygen species and apoptosis. Western blotting was conducted to detect the p53, Bax, cleaved caspase 9 and cleaved caspase 3 proteins. Celastrol inhibited tumour growth, induced apoptosis, and upregulated pro-apoptotic proteins in the xenograft tumour mouse model. Metabolomics showed that cysteine metabolism was the key metabolic alteration after celastrol treatment in HL-60 cells in vivo. Celastrol decreased L-cysteine in HL-60 cells. Acetylcysteine supplementation reversed reactive oxygen species accumulation and apoptosis induced by celastrol and reversed the dramatic decrease in the mitochondrial membrane potential and upregulation of pro-apoptotic proteins in HL-60 cells. In NB-4 cells, celastrol decreased L-cysteine, and acetylcysteine reversed celastrol-induced reactive oxygen species accumulation and apoptosis. We are the first to identify the involvement of a cysteine metabolism/reactive oxygen species/p53/Bax/caspase 9/caspase 3 pathway in celastrol-triggered mitochondrial apoptosis in HL-60 and NB-4 cells, providing a novel underlying mechanism through which celastrol could be used to treat acute myeloid leukaemia, especially acute promyelocytic leukaemia.

Somatostatin receptor mediated targeting of acute myeloid leukemia by photodynamic metal complexes for light induced apoptosis

Acute myeloid leukemia (AML) is characterized by relapse and treatment resistance in a major fraction of patients, underlining the need of innovative AML targeting therapies. Here we analysed the therapeutic potential of an innovative biohybrid consisting of the tumor-associated peptide somatostatin and the photosensitizer ruthenium in AML cell lines and primary AML patient samples. Selective toxicity was analyzed by using CD34 enriched cord blood cells as control. Treatment of OCI AML3, HL60 and THP1 resulted in a 92, and 99 and 97% decrease in clonogenic growth compared to the controls. Primary AML cells demonstrated a major response with a 74 to 99% reduction in clonogenicity in 5 of 6 patient samples. In contrast, treatment of CD34⁺ CB cells resulted in substantially less reduction in colony numbers. Subcellular localization assays of RU-SST in OCI-AML3 cells confirmed strong co-localization of RU-SST in the lysosomes compared to the other cellular organelles. Our data demonstrate that conjugation of a Ruthenium complex with somatostatin is efficiently eradicating LSC candidates of patients with AML. This indicates that receptor mediated lysosomal accumulation of photodynamic metal complexes is a highly attractive approach for targeting AML cells.

Cellular Stress-Modulating Drugs Can Potentially Be Identified by in Silico Screening with Connectivity Map (CMap)

Accompanied by increased life span, aging-associated diseases, such as metabolic diseases and cancers, have become serious health threats. Recent studies have documented that aging-associated diseases are caused by prolonged cellular stresses such as endoplasmic reticulum (ER) stress, mitochondrial stress, and oxidative stress. Thus, ameliorating cellular stresses could be an effective approach to treat aging-associated diseases and, more importantly, to prevent such diseases from happening. However, cellular stresses and their molecular responses within the cell are typically mediated by a variety of factors encompassing different signaling pathways. Therefore, a target-based drug discovery method currently being used widely (reverse pharmacology) may not be adequate to uncover novel drugs targeting cellular stresses and related diseases. The connectivity map (CMap) is an online pharmacogenomic database cataloging gene expression data from cultured cells treated individually with various chemicals, including a variety of phytochemicals. Moreover, by querying through CMap, researchers may screen registered chemicals in silico and obtain the likelihood of drugs showing a similar gene expression profile with desired and chemopreventive conditions. Thus, CMap is an effective genome-based tool to discover novel chemopreventive drugs.

Identification of chlorprothixene as a potential drug that induces apoptosis and autophagic cell death in acute myeloid leukemia cells

Although acute myeloid leukemia (AML) is a highly heterogeneous malignance, the common molecular mechanisms shared by different AML subtypes play critical roles in AML development. It is possible to identify new drugs that are effective for various AML subtypes based on the common molecular mechanisms. Therefore, we developed a hypothesis-driven bioinformatic drug screening framework by integrating multiple omics data. In this study, we identified that chlorprothixene, a dopamine receptor antagonist, could effectively inhibit growth of AML cells from different subtypes. RNA-seq analysis suggested that chlorprothixene perturbed a series of crucial biological processes such as cell cycle, apoptosis, and autophagy in AML cells. Further investigations indicated that chlorprothixene could induce both apoptosis and autophagy in AML cells, and apoptosis and autophagy could act as partners to induce cell death cooperatively. Remarkably, chlorprothixene was found to inhibit tumor growth and induce in situ leukemic cell apoptosis in the murine xenograft model. Furthermore, chlorprothixene treatment could reduce the level of oncofusion proteins PML-RARα and AML1-ETO, thus elevate the expression of apoptosis-related genes, and lead to AML cell death. Our results provided new insights for drug repositioning of AML therapy and confirmed that chlorprothixene might be a potential candidate for treatment of different subtypes of AML by reducing expression of oncofusion proteins. DATABASE: RNA-seq data are available in GEO database under the accession number GSE124316.

Rapid Discovery of Potential Drugs for Osteonecrosis of Femoral Head Based on Gene Expression Omnibus Database and Connectivity Map

Objective

To use Gene Expression Omnibus (GEO) database coupled with Connectivity Map (CMap) databases to screen potential therapeutic drugs for osteonecrosis of femoral head (ONFH) rapidly.

Methods

Raw genetic data with accession number GSE74089 that contained eight hip articular cartilage specimens from four ONFH patients and four healthy controls were obtained from the Gene Expression Omnibus (GEO) database and were then integrated using R to identify differentially expressed genes (DEGs). Subsequently, to identify several potential small molecular compounds that were most strongly negatively correlated with ONFH, a search query of DEGs was explored by using CMap.

Results

Filtering revealed 1937 DEGs with log (fold‐change) ≥1 and adjust P value < 0.001. Finally, a network of candidate targets for ONFH with 135 nodes and 660 edges was constructed through network topology analysis, including 96 up‐regulated genes and 39 down‐regulated genes. Several significant gene functions and signaling pathways associated with pathological processes of ONFH were identified via gene enrichment analysis. Based on the CMap database, some potential small molecular components that may be possible to counteract the effects of molecular signal imbalance for ONFH were identified. Neostigmine bromide with low CMap score and P value and specificity score was predicted to be the most candidate compound, involved in the “positive regulation of stem cell proliferation,” “regulation of protein autophosphorylation,” “VEGF signaling pathway,” and “ECM‐receptor interaction.”

Conclusions

The GEO and CMap databases can be effectively used in understanding the molecular changes in ONFH and provide a systematic manner to identify potential drugs for ONFH prevention and treatment. However, additional clinical and experimental research of the candidate compound is warranted.

Cancer Stem Cells in the Development of Novel Therapeutics for Refractory Pediatric Leukemia

Although treatment strategies for pediatric leukemia have improved overall survival rates in the recent past, relapse rates in certain subgroups such as infant leukemia remain unacceptably high. Despite undergoing extensive chemotherapy designed to target the rapidly proliferating leukemia cells, many of these children experience relapse. In refractory leukemia, the existence of cell populations with stemness characteristics, termed leukemia stem cells (LSCs), which remain quiescent and subsequently replenish the blast population, has been described. A significant body of evidence exists, derived largely from xenograft models of adult acute myeloid leukemia, to support the idea that LSCs may play a fundamental role in refractory disease. In addition, clinical studies have also linked LSCs with increased minimal residual disease, higher relapse rate, and decreased survival rates in these patients. Recently, a number of reports have addressed effective ways to utilize new-generation genomic sequencing and transcriptomic analyses to identify targeted therapeutic agents aimed at LSCs, while sparing normal hematopoietic stem cells. These data underscore the value of timely translation of knowledge from adult studies to the unique molecular and physiological characteristics seen in pediatric leukemia. We aim to summarize this article in the rapidly expanding field of stem cell biology in hematopoietic malignancies, focusing particularly on relevant preclinical models and novel targeted therapeutics, and their applicability to childhood leukemia.

Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives

The discovery of new pharmaceutical drugs is one of the preeminent tasks-scientifically, economically, and socially-in biomedical research. Advances in informatics and computational biology have increased productivity at many stages of the drug discovery pipeline. Nevertheless, drug discovery has slowed, largely due to the reliance on small molecules as the primary source of novel hypotheses. Natural products (such as plant metabolites, animal toxins, and immunological components) comprise a vast and diverse source of bioactive compounds, some of which are supported by thousands of years of traditional medicine, and are largely disjoint from the set of small molecules used commonly for discovery. However, natural products possess unique characteristics that distinguish them from traditional small molecule drug candidates, requiring new methods and approaches for assessing their therapeutic potential. In this review, we investigate a number of state-of-the-art techniques in bioinformatics, cheminformatics, and knowledge engineering for data-driven drug discovery from natural products. We focus on methods that aim to bridge the gap between traditional small-molecule drug candidates and different classes of natural products. We also explore the current informatics knowledge gaps and other barriers that need to be overcome to fully leverage these compounds for drug discovery. Finally, we conclude with a "road map" of research priorities that seeks to realize this goal.

Traditional Chinese medicine as a cancer treatment: Modern perspectives of ancient but advanced science

Traditional Chinese medicine (TCM) has been practiced for thousands of years and at the present time is widely accepted as an alternative treatment for cancer. In this review, we sought to summarize the molecular and cellular mechanisms underlying the chemopreventive and therapeutic activity of TCM, especially that of the Chinese herbal medicine-derived phytochemicals curcumin, resveratrol, and berberine. Numerous genes have been reported to be involved when using TCM treatments and so we have selectively highlighted the role of a number of oncogene and tumor suppressor genes in TCM therapy. In addition, the impact of TCM treatment on DNA methylation, histone modification, and the regulation of noncoding RNAs is discussed. Furthermore, we have highlighted studies of TCM therapy that modulate the tumor microenvironment and eliminate cancer stem cells. The information compiled in this review will serve as a solid foundation to formulate hypotheses for future studies on TCM-based cancer therapy.

Gene expression profile-based drug screen identifies SAHA as a novel treatment for NAFLD

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide.

Synthesis and biological evaluation of dithiocarbamate esters of parthenolide as potential anti-acute myelogenous leukaemia agents

A series of dithiocarbamate esters of parthenolide (PTL) was designed, synthesised, and evaluated for their anti- acute myelogenous leukaemia (AML) activities. The most promising compound 7l showed greatly improved potency against AML progenitor cell line KG1a with IC₅₀ value of 0.7 μM, and the efficacy was 8.7-folds comparing to that of PTL (IC₅₀ = 6.1 μM). Compound 7l induced apoptosis of total primary human AML cells and leukaemia stem cell (LSCs) of primary AML cells while sparing normal cells. Furthermore, 7l suppressed the colony formation of primary human leukaemia cells. Moreover, compound 12, the salt form of 7l, prolonged the lifespan of mice in two patient-derived xenograft models and had no observable toxicity. The preliminary molecular mechanism study revealed that 7l-mediated apoptosis is associated with mitogen-activated protein kinase signal pathway. On the basis of these investigations, we propose that 12 might be a promising drug candidate for ultimate discovery of anti-LSCs drug.

Identification of key candidate genes and small molecule drugs in cervical cancer by bioinformatics strategy

Purpose

Cervical cancer (CC) is one of the most common malignant tumors among women. The present study aimed at integrating two expression profile datasets to identify critical genes and potential drugs in CC.

Materials and methods

Expression profiles, GSE7803 and GSE9750, were integrated using bioinformatics methods, including differentially expressed genes analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and protein–protein interaction (PPI) network construction. Subsequently, survival analysis was performed among the key genes using Gene Expression Profiling Interactive Analysis websites. Connectivity Map (CMap) was used to query potential drugs for CC.

Results

A total of 145 upregulated genes and 135 downregulated genes in CC were identified. The functional changes of these differentially expressed genes related to CC were mainly associated with cell cycle, DNA replication, p53 signaling pathway, and oocyte meiosis. A PPI network was identified by STRING with 220 nodes and 2,111 edges. Thirteen key genes were identified as the intersecting genes of the enrichment pathways and the top 20 nodes in PPI network. Survival analysis revealed that high mRNA expression of MCM2, PCNA, and RFC4 was significantly associated with longer overall survival, and the survival was significantly better in the low-expression RRM2 group. Moreover, CMap predicted nine small molecules as possible adjuvant drugs to treat CC.

Conclusion

Our study found key dysregulated genes involved in CC and potential drugs to combat it, which might provide insights into CC pathogenesis and might shed light on potential CC treatments.

Acute Myeloid Leukemia: Is That All There Is?

Acute myeloid leukemia (AML) is characterized by the clonal proliferation of malignant myeloid blast cells in the marrow along with impaired normal hematopoiesis. With an almost stagnant approach for the management of patients with AML in the last three decades, the main purpose of this paper is to increase our understanding of recent scientific advancements for the enhanced diagnosis and treatment of AML. Existing research data related to different approaches for a possible improvement in AML management has been collected and discussed. The identification of recurrently mutated genes, such as CCAAT-enhancer-binding proteins α (CEBPα), Fms-related tyrosine kinase 3 (FLT3), and nucleophosmin 1 (NPM1) along with the classic diagnostic karyotype has improved prognostic-risk stratification. Moreover, mutations affecting cellular metabolism like isocitrate dehydrogenase (IDH1), lysine-specific demethylase 1 (LSD 1), and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) have become a huge success by providing targets for novel therapeutic drugs. Checkpoint inhibitors (CPI) and vaccination against tumor-associated antigen are added options considered, which require further trials before their efficacy can be determined. An important tool in monitoring early response to therapy, minimal residual disease (MRD) assays can be further refined by including pretreatment parameters such as cytogenetic and molecular markers. Potential side effects and resistance to treatment remains a huge barrier in completely finding success against AML and work needs to be done to find combinations of immunotherapies to possibly reduce adaptive resistance by AML.

On the overestimation of random forest's out-of-bag error

The ensemble method random forests has become a popular classification tool in bioinformatics and related fields. The out-of-bag error is an error estimation technique often used to evaluate the accuracy of a random forest and to select appropriate values for tuning parameters, such as the number of candidate predictors that are randomly drawn for a split, referred to as mtry. However, for binary classification problems with metric predictors it has been shown that the out-of-bag error can overestimate the true prediction error depending on the choices of random forests parameters. Based on simulated and real data this paper aims to identify settings for which this overestimation is likely. It is, moreover, questionable whether the out-of-bag error can be used in classification tasks for selecting tuning parameters like mtry, because the overestimation is seen to depend on the parameter mtry. The simulation-based and real-data based studies with metric predictor variables performed in this paper show that the overestimation is largest in balanced settings and in settings with few observations, a large number of predictor variables, small correlations between predictors and weak effects. There was hardly any impact of the overestimation on tuning parameter selection. However, although the prediction performance of random forests was not substantially affected when using the out-of-bag error for tuning parameter selection in the present studies, one cannot be sure that this applies to all future data. For settings with metric predictor variables it is therefore strongly recommended to use stratified subsampling with sampling fractions that are proportional to the class sizes for both tuning parameter selection and error estimation in random forests. This yielded less biased estimates of the true prediction error. In unbalanced settings, in which there is a strong interest in predicting observations from the smaller classes well, sampling the same number of observations from each class is a promising alternative.

Lipid Peroxidation-Derived Aldehydes, 4-Hydroxynonenal and Malondialdehyde in Aging-Related Disorders

Among the various mechanisms involved in aging, it was proposed long ago that a prominent role is played by oxidative stress. A major way by which the latter can provoke structural damage to biological macromolecules, such as DNA, lipids, and proteins, is by fueling the peroxidation of membrane lipids, leading to the production of several reactive aldehydes. Lipid peroxidation-derived aldehydes can not only modify biological macromolecules, by forming covalent electrophilic addition products with them, but also act as second messengers of oxidative stress, having relatively extended lifespans. Their effects might be further enhanced with aging, as their concentrations in cells and biological fluids increase with age. Since the involvement and the role of lipid peroxidation-derived aldehydes, particularly of 4-hydroxynonenal (HNE), in neurodegenerations, inflammation, and cancer, has been discussed in several excellent recent reviews, in the present one we focus on the involvement of reactive aldehydes in other age-related disorders: osteopenia, sarcopenia, immunosenescence and myelodysplastic syndromes. In these aging-related disorders, characterized by increases of oxidative stress, both HNE and malondialdehyde (MDA) play important pathogenic roles. These aldehydes, and HNE in particular, can form adducts with circulating or cellular proteins of critical functional importance, such as the proteins involved in apoptosis in muscle cells, thus leading to their functional decay and acceleration of their molecular turnover and functionality. We suggest that a major fraction of the toxic effects observed in age-related disorders could depend on the formation of aldehyde-protein adducts. New redox proteomic approaches, pinpointing the modifications of distinct cell proteins by the aldehydes generated in the course of oxidative stress, should be extended to these age-associated disorders, to pave the way to targeted therapeutic strategies, aiming to alleviate the burden of morbidity and mortality associated with these disturbances.

Leukemic stem cell signatures identify novel therapeutics targeting acute myeloid leukemia

Therapy for acute myeloid leukemia (AML) involves intense cytotoxic treatment and yet approximately 70% of AML are refractory to initial therapy or eventually relapse. This is at least partially driven by the chemo-resistant nature of the leukemic stem cells (LSCs) that sustain the disease, and therefore novel anti-LSC therapies could decrease relapses and improve survival. We performed in silico analysis of highly prognostic human AML LSC gene expression signatures using existing datasets of drug–gene interactions to identify compounds predicted to target LSC gene programs. Filtering against compounds that would inhibit a hematopoietic stem cell (HSC) gene signature resulted in a list of 151 anti-LSC candidates. Using a novel in vitro LSC assay, we screened 84 candidate compounds at multiple doses and confirmed 14 drugs that effectively eliminate human AML LSCs. Three drug families presenting with multiple hits, namely antihistamines (astemizole and terfenadine), cardiac glycosides (strophanthidin, digoxin and ouabain) and glucocorticoids (budesonide, halcinonide and mometasone), were validated for their activity against human primary AML samples. Our study demonstrates the efficacy of combining computational analysis of stem cell gene expression signatures with in vitro screening to identify novel compounds that target the therapy-resistant LSC at the root of relapse in AML.

A review of connectivity map and computational approaches in pharmacogenomics

Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

A review of connectivity map and computational approaches in pharmacogenomics

Large-scale perturbation databases, such as Connectivity Map (CMap) or Library of Integrated Network-based Cellular Signatures (LINCS), provide enormous opportunities for computational pharmacogenomics and drug design. A reason for this is that in contrast to classical pharmacology focusing at one target at a time, the transcriptomics profiles provided by CMap and LINCS open the door for systems biology approaches on the pathway and network level. In this article, we provide a review of recent developments in computational pharmacogenomics with respect to CMap and LINCS and related applications.

Cell-specific prediction and application of drug-induced gene expression profiles

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

Hsp72 Is an Intracellular Target of the α,β-Unsaturated Sesquiterpene Lactone, Parthenolide

The electrophilic natural product parthenolide has generated significant interest as a model for potential chemotherapeutics. Similar to other α,β-unsaturated carbonyl electrophiles, parthenolide induces the heat shock response in leukemia cells, potentially through covalent adduction of heat shock proteins. Other thiol-reactive electrophiles have also been shown to induce the heat shock response as well as to covalently adduct members of the heat shock protein family, such as heat shock protein 72 (Hsp72). To identify sites of modification of Hsp72 by parthenolide, we used high-resolution tandem mass spectrometry to detect 10 lysine, histidine, and cysteine residues of recombinant Hsp72 as modified in vitro by 10 and 100 μM parthenolide. To further ascertain that modification of Hsp72 by parthenolide occurs inside cells and not simply as an in vitro artifact, an alkyne-labeled derivative of parthenolide was synthesized to enable enrichment and detection of protein targets of parthenolide using copper-catalyzed [3 + 2] azide-alkyne cycloaddition. The alkyne-labeled parthenolide derivative displays an half maximal inhibitory concentration (IC₅₀) in undifferentiated acute monocytic leukemia cells (THP-1) of 13.1 ± 1.1 μM, whereas parthenolide has an IC₅₀ of 4.7 ± 1.1 μM. Concentration dependence of protein modification by the alkyne-parthenolide derivative was demonstrated, as well as in vitro adduction of Hsp72. Following treatment of THP-1 cells in culture by the alkyne-parthenolide, adducted proteins were isolated with neutravidin resin and detected by immunoblotting in the enriched protein fraction. Hsp70 proteins were detected in the enriched proteins, indicating that Hsp70 proteins were adducted intracellularly by the alkyne-parthenolide derivative.

Evaluation of connectivity map-discovered celastrol as a radiosensitizing agent in a murine lung carcinoma model: Feasibility study of diffusion-weighted magnetic resonance imaging

This study was designed to identify potential radiosensitizing (RS) agents for combined radio- and chemotherapy in a murine model of human lung carcinoma, and to evaluate the in vivo effect of the RS agents using diffusion-weighted magnetic resonance imaging (DW-MRI). Radioresistance-associated genes in A549 and H460 cells were isolated on the basis of their gene expression profiles. Celastrol was selected as a candidate RS by using connectivity mapping, and its efficacy in lung cancer radiotherapy was tested. Mice inoculated with A549 carcinoma cells were treated with single ionizing radiation (SIR), single celastrol (SC), or celastrol-combined ionizing radiation (CCIR). Changes in radiosensitization over time were assessed using DW-MRI before and at 3, 6, and 12 days after therapy initiation. The tumors were stained with hematoxylin and eosin at 6 and 12 days after therapy. The percentage change in the apparent diffusion coefficient (ADC) value in the CCIR group was significantly higher than that in the SC and SIR group on the 12^th day (Mann–Whitney U-test, p = 0.05; Kruskal–Wallis test, p < 0.05). A significant correlation (Spearman’s rho correlation coefficient of 0.713, p = 0.001) was observed between the mean percentage tumor necrotic area and the mean ADC values after therapy initiation. These results suggest that the novel radiosensitizing agent celastrol has therapeutic effects when combined with ionizing radiation (IR), thereby maximizing the therapeutic effect of radiation in non–small cell lung carcinoma. In addition, DW-MRI is a useful noninvasive tool to monitor the effects of RS agents by assessing cellularity changes and sequential therapeutic responses.

Targeted therapy for a subset of acute myeloid leukemias that lack expression of aldehyde dehydrogenase 1A1

Aldehyde dehydrogenase 1A1 (ALDH1A1) activity is high in hematopoietic stem cells and functions in part to protect stem cells from reactive aldehydes and other toxic compounds. In contrast, we found that approximately 25% of all acute myeloid leukemias expressed low or undetectable levels of ALDH1A1 and that this ALDH1A1⁻ subset of leukemias correlates with good prognosis cytogenetics. ALDH1A1⁻ cell lines as well as primary leukemia cells were found to be sensitive to treatment with compounds that directly and indirectly generate toxic ALDH substrates including 4-hydroxynonenal and the clinically relevant compounds arsenic trioxide and 4-hydroperoxycyclophosphamide. In contrast, normal hematopoietic stem cells were relatively resistant to these compounds. Using a murine xenotransplant model to emulate a clinical treatment strategy, established ALDH1A1⁻ leukemias were also sensitive to in vivo treatment with cyclophosphamide combined with arsenic trioxide. These results demonstrate that targeting ALDH1A1⁻ leukemic cells with toxic ALDH1A1 substrates such as arsenic and cyclophosphamide may be a novel targeted therapeutic strategy for this subset of acute myeloid leukemias.

Peperomin E and its synthetic amino derivatives: potent agents targeting leukaemia stem cells

A series of amino derivatives of Peperomin E have been synthesized. Compound 6, derived from N-methylethanolamine, exhibited exclusive cytotoxicity against leukaemia stem cells (IC₅₀ = 0.5 μM) and low toxicity against normal bone marrow cells.

In silico frameworks for systematic pre-clinical screening of potential anti-leukemia therapeutics

INTRODUCTION

Leukemia is a collection of highly heterogeneous cancers that arise from neoplastic transformation and clonal expansion of immature hematopoietic cells. Post-treatment recurrence is high, especially among elderly patients, thus necessitating more effective treatment modalities. Development of novel anti-leukemic compounds relies heavily on traditional in vitro screens which require extensive resources and time. Therefore, integration of in silico screens prior to experimental validation can improve the efficiency of pre-clinical drug development. Areas covered: This article reviews different methods and frameworks used to computationally screen for anti-leukemic agents. In particular, three approaches are discussed including molecular docking, transcriptomic integration, and network analysis. Expert opinion: Today's data deluge presents novel opportunities to develop computational tools and pipelines to screen for likely therapeutic candidates in the treatment of leukemia. Formal integration of these methodologies can accelerate and improve the efficiency of modern day anti-leukemic drug discovery and ease the economic and healthcare burden associated with it.

Novel strategies for targeting leukemia stem cells: sounding the death knell for blood cancer

BACKGROUND

Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are characterized by high self-renewal and multi-lineage differentiation capacities. CSCs are thought to play indispensable roles in the initiation, progression and metastasis of many types of cancer. Leukemias are thought to be initiated and maintained by a specific sub-type of CSC, the leukemia stem cell (LSC). An important feature of LSCs is their resistance to standard therapy, which may lead to relapse. Increasing efforts are aimed at developing novel therapeutic strategies that selectively target LSCs, while sparing their normal counterparts and, thus, minimizing adverse treatment-associated side-effects. These LSC targeting therapies aim to eradicate LSCs through affecting mechanisms that control their survival, self-renewal, differentiation, proliferation and cell cycle progression. Some LSC targeting therapies have already been proven successful in pre-clinical studies and they are now being tested in clinical studies, mainly in combination with conventional treatment regimens.

CONCLUSIONS

A growing body of evidence indicates that the selective targeting of LSCs represents a promising approach to improve disease outcome. Beyond doubt, the CSC hypothesis has added a new dimension to the area of anticancer research, thereby paving the way for shaping a new trend in cancer therapy.

Alantolactone selectively ablates acute myeloid leukemia stem and progenitor cells

BACKGROUND

The poor outcomes for patients diagnosed with acute myeloid leukemia (AML) are largely attributed to leukemia stem cells (LSCs) which are difficult to eliminate with conventional therapy and responsible for relapse. Thus, new therapeutic strategies which could selectively target LSCs in clinical leukemia treatment and avoid drug resistance are urgently needed. However, only a few small molecules have been reported to show anti-LSCs activity.

METHODS

The aim of the present study was to identify alantolactone as novel agent that can ablate acute myeloid leukemia stem and progenitor cells from AML patient specimens and evaluate the anticancer activity of alantolactone in vitro and in vivo.

RESULTS

The present study is the first to demonstrate that alantolactone, a prominent eudesmane-type sesquiterpene lactone, could specifically ablate LSCs from AML patient specimens. Furthermore, in comparison to the conventional chemotherapy drug, cytosine arabinoside (Ara-C), alantolactone showed superior effects of leukemia cytotoxicity while sparing normal hematopoietic cells. Alantolactone induced apoptosis with a dose-dependent manner by suppression of NF-kB and its downstream target proteins. DMA-alantolactone, a water-soluble prodrug of alantolactone, could suppress tumor growth in vivo.

CONCLUSIONS

Based on these results, we propose that alantolactone may represent a novel LSCs-targeted therapy and eudesmane-type sesquiterpene lactones offer a new scaffold for drug discovery towards anti-LSCs agents.

Rational Design of a Parthenolide-based Drug Regimen That Selectively Eradicates Acute Myelogenous Leukemia Stem Cells

Although multidrug approaches to cancer therapy are common, few strategies are based on rigorous scientific principles. Rather, drug combinations are largely dictated by empirical or clinical parameters. In the present study we developed a strategy for rational design of a regimen that selectively targets human acute myelogenous leukemia (AML) stem cells. As a starting point, we used parthenolide, an agent shown to target critical mechanisms of redox balance in primary AML cells. Next, using proteomic, genomic, and metabolomic methods, we determined that treatment with parthenolide leads to induction of compensatory mechanisms that include up-regulated NADPH production via the pentose phosphate pathway as well as activation of the Nrf2-mediated oxidative stress response pathway. Using this knowledge we identified 2-deoxyglucose and temsirolimus as agents that can be added to a parthenolide regimen as a means to inhibit such compensatory events and thereby further enhance eradication of AML cells. We demonstrate that the parthenolide, 2-deoxyglucose, temsirolimus (termed PDT) regimen is a potent means of targeting AML stem cells but has little to no effect on normal stem cells. Taken together our findings illustrate a comprehensive approach to designing combination anticancer drug regimens.

An integrated network platform for contextual prioritization of drugs and pathways

Repurposing of drugs to novel disease indications has promise for faster clinical translation. However, identifying the best drugs for a given pathological context is not trivial. We developed an integrated random walk-based network framework that combines functional biomolecular relationships and known drug-target interactions as a platform for contextual prioritization of drugs, genes and pathways. We show that the use of gene-centric or drug-centric data, such as gene expression data or a phenotypic drug screen, respectively, within this network platform can effectively prioritize drugs and pathways, respectively, to the studied biological context. We demonstrate that various genomic data can be used as contextual cues to effectively prioritize drugs to the studied context, while similarly, phenotypic drug screen data can be used to effectively prioritize genes and pathways to the studied phenotypic context. As a proof-of-principle, we showcase the use of our platform to identify known and novel drug indications against different subsets of breast cancers through contextual prioritization based on genome-wide gene expression, shRNA and drug screen and clinical survival data. The integrated network and associated methods are incorporated into the NetWalker suite for functional genomics analysis ().

Gene Expression-Based Screen for Parkinson's Disease Identifies GW8510 as a Neuroprotective Agent

We carried out a gene expression-based in silico screen in order to identify small molecules with gene-expression profiles that are anticorrelated with a gene-expression profile for Parkinson's disease (PD). We identified the cyclin-dependent kinase 2/5 (CDK2/5) inhibitor GW8510 as our most significant hit and characterized its effects in rodent MN9D cells and in human neuronal cells derived from induced pluripotent stem cells. GW8510 demonstrated neuroprotective ability in MN9D cells in the presence of 1-methyl-4-phenylpyridium (MPP(+)), a widely used neurotoxin model for Parkinson's disease. In order to delineate the nature and extent of GW8510's neuroprotective properties, we studied GW8510 in human neuronal cells in the context of various mechanisms of cellular stress. We found that GW8510 was protective against small-molecule mitochondrial and endoplasmic reticulum stressors. Our findings illustrate an approach to using small-molecule gene expression libraries to identify compounds with therapeutic potential in human diseases.

The NF-κB Pathway and Cancer Stem Cells

The NF-κB transcription factor pathway is a crucial regulator of inflammation and immune responses. Additionally, aberrant NF-κB signaling has been identified in many types of cancer. Downstream of key oncogenic pathways, such as RAS, BCR-ABL, and Her2, NF-κB regulates transcription of target genes that promote cell survival and proliferation, inhibit apoptosis, and mediate invasion and metastasis. The cancer stem cell model posits that a subset of tumor cells (cancer stem cells) drive tumor initiation, exhibit resistance to treatment, and promote recurrence and metastasis. This review examines the evidence for a role for NF-κB signaling in cancer stem cell biology.

Parthenolide Induces Apoptosis in Committed Progenitor AML Cell line U937 via Reduction in Osteopontin

BACKGROUND

Interfering with cell proliferation and survival is a critical role for antineoplastic drugs leading to cell death through induction of apoptosis. Alternative treatments with herbal extracts offer insights into acute myeloid leukemia (AML) therapy. Parthenolide (PTL), an extract from feverfew, induces apoptosis in primary human leukemia stem cells (LSCs) and bulk leukemic cell populations. Osteopontin (OPN) preserves cell viability in response to anticancer agents and its receptors could be utilized for therapeutic targeting of cancer cells.

METHODS

U937 cells were cultured in RPMI 1640 with concentrations of 2, 4, 6, 8, and 10 µM PTL for 20-24 hours for MTT assays. Apoptosis assays were performed with Annexin V-Alexa Fluor-488/PI as Annexin V+/PI- and Annexin V+/PI+ to measure early and late apoptosis, respectively. Quantitative real-time PCR was used to measure OPN gene expression using the 2(-ΔΔCt) method. The PTL-treated cells were stained with FITC-CD38 antibody for flow cytometry analyses. Data were compared using one-way analysis of variance (ANOVA) by SPSS 19.

RESULTS

Parthenolide inhibited growth of U937 cells with IC25 and IC50 values of 4 and 5.8 µM, respectively. Death induction with PTL was apoptotic. Flow cytometry showed a significant decrease in the percentage of CD38+ U937 cells in response to PTL. Osteopontin gene expression decreased in response to PTL.

CONCLUSION

PTL induced apoptosis and reduced OPN gene expression in U937 cells.

Integrated Drug Expression Analysis for leukemia: an integrated in silico and in vivo approach to drug discovery

Screening for drug compounds that exhibit therapeutic properties in the treatment of various diseases remains a challenge even after considerable advancements in biomedical research. Here, we introduce an integrated platform that exploits gene expression compendia generated from drug-treated cell lines and primary tumor tissue to identify therapeutic candidates that can be used in the treatment of acute myeloid leukemia (AML). Our framework combines these data with patient survival information to identify potential candidates that presumably have a significant impact on AML patient survival. We use a drug regulatory score (DRS) to measure the similarity between drug-induced cell line and patient tumor gene expression profiles, and show that these computed scores are highly correlated with in vitro metrics of pharmacological activity. Furthermore, we conducted several in vivo validation experiments of our potential candidate drugs in AML mouse models to demonstrate the accuracy of our in silico predictions.

Synthesis and mode of action of oligomeric sesquiterpene lactones

In this highlight we describe two case studies from our laboratory, involving the biomimetic syntheses and the biological mechanism elucidation of the bioactive oligomeric sesquiterpenoids, (+)-ainsliadimer A (4) and (−)-ainsliatrimer A (5).

Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials

BACKGROUND

The presence of diverse types of nanomaterials (NMs) in commerce is growing at an exponential pace. As a result, human exposure to these materials in the environment is inevitable, necessitating the need for rapid and reliable toxicity testing methods to accurately assess the potential hazards associated with NMs. In this study, we applied biclustering and gene set enrichment analysis methods to derive essential features of altered lung transcriptome following exposure to NMs that are associated with lung-specific diseases. Several datasets from public microarray repositories describing pulmonary diseases in mouse models following exposure to a variety of substances were examined and functionally related biclusters of genes showing similar expression profiles were identified. The identified biclusters were then used to conduct a gene set enrichment analysis on pulmonary gene expression profiles derived from mice exposed to nano-titanium dioxide (nano-TiO2), carbon black (CB) or carbon nanotubes (CNTs) to determine the disease significance of these data-driven gene sets.

RESULTS

Biclusters representing inflammation (chemokine activity), DNA binding, cell cycle, apoptosis, reactive oxygen species (ROS) and fibrosis processes were identified. All of the NM studies were significant with respect to the bicluster related to chemokine activity (DAVID; FDR p-value = 0.032). The bicluster related to pulmonary fibrosis was enriched in studies where toxicity induced by CNT and CB studies was investigated, suggesting the potential for these materials to induce lung fibrosis. The pro-fibrogenic potential of CNTs is well established. Although CB has not been shown to induce fibrosis, it induces stronger inflammatory, oxidative stress and DNA damage responses than nano-TiO2 particles.

CONCLUSION

The results of the analysis correctly identified all NMs to be inflammogenic and only CB and CNTs as potentially fibrogenic. In addition to identifying several previously defined, functionally relevant gene sets, the present study also identified two novel genes sets: a gene set associated with pulmonary fibrosis and a gene set associated with ROS, underlining the advantage of using a data-driven approach to identify novel, functionally related gene sets. The results can be used in future gene set enrichment analysis studies involving NMs or as features for clustering and classifying NMs of diverse properties.