Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance

Gene ontology (GO) is an eminent knowledge base frequently used for providing biological interpretations for the analysis of genes or gene sets from biological, medical and clinical problems. Unfortunately, the interpretation of such results is challenging due to the large number of GO terms, their hierarchical and connected organization as directed acyclic graphs (DAGs) and the lack of tools allowing to exploit this structural information explicitly. For this reason, we developed the R package GOxploreR. The main features of GOxploreR are (I) easy and direct access to structural features of GO, (II) structure-based ranking of GO-terms, (III) mapping to reduced GO-DAGs including visualization capabilities and (IV) prioritizing of GO-terms. The underlying idea of GOxploreR is to exploit a graph-theoretical perspective of GO as manifested by its DAG-structure and the containing hierarchy levels for cumulating semantic information. That means all these features enhance the utilization of structural information of GO and complement existing analysis tools. Overall, GOxploreR provides exploratory as well as confirmatory tools for complementing any kind of analysis resulting in a list of GO-terms, e.g., from differentially expressed genes or gene sets, GWAS or biomarkers. Our R package GOxploreR is freely available from CRAN.

Antiproliferative and apoptotic effects of indole derivative, N-(2-hydroxy-5-nitrophenyl (4'-methylphenyl) methyl) indoline in breast cancer cells

Indoline derivatives functions as an inhibitors of epidermal growth factor receptor (EGFR) with the anticancer potential against various cancers. We aim to investigate anti-breast cancer effects and mechanism of action of novel indoline derivatives. Molecular docking of seven indoline derivates with EGFR revealed, N-(2-hydroxy-5-nitrophenyl (4'-methylphenyl) methyl) indoline (HNPMI) as the top lead compound. RT-PCR analysis showed the downregulation of PI3K/S6K1 genes in breast cancer cells through the activation of EGFR with HNPMI. This compound found to have higher cytotoxicity than Cyclophosphamide, with the IC₅₀ of 64.10 μM in MCF-7 and 119.99 μM in SkBr3 cells. HNPMI significantly reduced the cell proliferation of MCF-7 and SkBr3 cells, without affecting non-cancerous cells, H9C2. Induction of apoptosis was analyzed by Caspase-3 and -9, DNA fragmentation, AO/EtBr staining and flow cytometry assays. A fold change of 0.218- and 0.098- for caspase-3 and 0.478- and 0.269- for caspase-9 in MCF7 and SkBr3 cells was observed, respectively. Caspase mediated apoptosis caused DNA fragmentation in breast cancer cells upon HNPMI treatment. The structural elucidation of HNPMI by QSAR model and ADME-Tox suggests, a bi-molecular interaction of HNPMI-EGFR which is related to antiproliferative and apoptotic activity. The data concludes that, HNPMI-induced the apoptosis via EGFR signaling pathway in breast cancer cells. Thus, HNPMI might serve as a scaffold for developing a potential anti-breast cancer therapeutic agent.

Anticancer activity of THMPP: Downregulation of PI3K/ S6K1 in breast cancer cell line

Breast cancer is the most common cancer that majorly affects female. The present study is focused on exploring the potential anticancer activity of 2-((1, 2, 3, 4-Tetrahydroquinolin-1-yl) (4 methoxyphenyl) methyl) phenol (THMPP), against human breast cancer. The mechanism of action, activation of specific signaling pathways, structural activity relationship and drug-likeness properties of THMPP remains elusive. Cell proliferation and viability assay, caspase enzyme activity, DNA fragmentation and FITC/Annexin V, AO/EtBr staining, RT-PCR, QSAR and ADME analysis were executed to understand the mode of action of the drug. The effect of THMPP on multiple breast cancer cell lines (MCF-7 and SkBr3), and non-tumorigenic cell line (H9C2) was assessed by MTT assay. THMPP at IC50 concentration of 83.23 μM and 113.94 μM, induced cell death in MCF-7 and SkBr3 cells, respectively. Increased level of caspase-3 and -9, fragmentation of DNA, translocation of phosphatidylserine membrane and morphological changes in the cells confirmed the effect of THMPP in inducing the apoptosis. Gene expression analysis has shown that THMPP was able to downregulate the expression of PI3K/S6K1 genes, possibly via EGFR signaling pathway in both the cell lines, MCF-7 and SkBr3. Further, molecular docking also confirms the potential binding of THMPP with EGFR. QSAR and ADME analysis proved THMPP as an effective anti-breast cancer drug, exhibiting important pharmacological properties. Overall, the results suggest that THMPP induced cell death might be regulated by EGFR signaling pathway which augments THMPP being developed as a potential candidate for treating breast cancer.

Combining deep learning with token selection for patient phenotyping from electronic health records

Artificial intelligence provides the opportunity to reveal important information buried in large amounts of complex data. Electronic health records (eHRs) are a source of such big data that provide a multitude of health related clinical information about patients. However, text data from eHRs, e.g., discharge summary notes, are challenging in their analysis because these notes are free-form texts and the writing formats and styles vary considerably between different records. For this reason, in this paper we study deep learning neural networks in combination with natural language processing to analyze text data from clinical discharge summaries. We provide a detail analysis of patient phenotyping, i.e., the automatic prediction of ten patient disorders, by investigating the influence of network architectures, sample sizes and information content of tokens. Importantly, for patients suffering from Chronic Pain, the disorder that is the most difficult one to classify, we find the largest performance gain for a combined word- and sentence-level input convolutional neural network (ws-CNN). As a general result, we find that the combination of data quality and data quantity of the text data is playing a crucial role for using more complex network architectures that improve significantly beyond a word-level input CNN model. From our investigations of learning curves and token selection mechanisms, we conclude that for such a transition one requires larger sample sizes because the amount of information per sample is quite small and only carried by few tokens and token categories. Interestingly, we found that the token frequency in the eHRs follow a Zipf law and we utilized this behavior to investigate the information content of tokens by defining a token selection mechanism. The latter addresses also issues of explainable AI.

Ensuring Quality Standards and Reproducible Research for Data Analysis Services in Oncology: A Cooperative Service Model

Modern molecular high-throughput devices, e.g., next-generation sequencing, have transformed medical research. Resulting data sets are usually high-dimensional on a genomic-scale providing multi-factorial information from intertwined molecular and cellular activities of genes and their products. This genomics-revolution installed precision medicine offering breathtaking opportunities for patient's diagnosis and treatment. However, due to the speed of these developments the quality standards of the involved data analyses are lacking behind, as exemplified by the infamous Duke Saga. In this paper, we argue in favor of a two-stage cooperative serve model that couples data generation and data analysis in the most beneficial way from the perspective of a patient to ensure data analysis quality standards including reproducible research.

Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

BACKGROUND

Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell-cell and metabolic coupling lead to functionally optimized structures is still limited.

RESULTS

Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this purpose, we first developed and parameterized a dynamic cell state and growth model for yeast based on on experimental data from homogeneous liquid media conditions. The inferred model is subsequently used in a spatially coarse-grained model for colony development to investigate the effect of metabolic coupling by calibrating spatial parameters from experimental time-course data of colony growth using state-of-the-art statistical techniques for model uncertainty and parameter estimations. The model is finally validated by independent experimental data of an alternative yeast strain with distinct metabolic characteristics and illustrates the impact of metabolic coupling for structure formation.

CONCLUSIONS

We introduce a novel model for yeast colony formation, present a statistical methodology for model calibration in a data-driven manner, and demonstrate how the established model can be used to generate predictions across scales by validation against independent measurements of genetically distinct yeast strains.

Battling Glioblastoma: A Novel Tyrosine Kinase Inhibitor with Multi-Dimensional Anti-Tumor Effect (Running Title: Cancer Cells Death Signalling Activation)

Glioblastoma (GB), a grade IV glioma, with high heterogeneity and chemoresistance, obligates a multidimensional antagonist to debilitate its competence. Considering the previous reports on thioesters as antitumor compounds, this paper investigates on use of this densely functionalized sulphur rich molecule as a potent anti-GB agent. Bio-evaluation of 12 novel compounds, containing α-thioether ketone and orthothioester functionalities, identified that five analogs exhibited better cytotoxic profile compared to standard drug cisplatin. Detailed toxicity studies of top compound were evaluated in two cell lines, using cell viability test, apoptotic activity, oxidative stress and caspase activation and RNA-sequencing analysis, to obtain a comprehensive molecular profile of drug activity. The most effective molecule presented half maximal inhibitory concentration (IC₅₀) values of 27 μM and 23 μM against U87 and LN229 GB cells, respectively. Same compound effectively weakened various angiogenic pathways, mainly MAPK and JAK-STAT pathways, downregulating VEGF. Transcriptome analysis identified significant promotion of apoptotic genes, and genes involved in cell cycle arrest, with concurrent inhibition of various tyrosine kinase cascades and stress response genes. Docking and immunoblotting studies suggest EGFR as a strong target of the orthothioester identified. Therefore, orthothioesters can potentially serve as a multi-dimensional chemotherapeutic possessing strong cytotoxic, anti-angiogenic and chemo-sensitization activity, challenging glioblastoma pathogenesis.

Utilizing Social Media Data for Psychoanalysis to Study Human Personality

Social media data, for instance from Twitter or Facebook, provide a new type of data that consist of a mixture of text, image and video information. From a scientific point of view, the capabilities of this type of data from such microblogs are not well explored and to date it is largely unknown what principal knowledge can be extracted thereof. In this paper, we present a discussion of the capabilities of data from microblogs for performing a psychoanalysis. This could allow an analysis of the human personality of individual users. Such prospects raises serious concerns regarding the privacy of users of social media platforms.

In vitro anti-glioblastoma activity of L-valine derived boroxazolidones

In the present study, a series of L-valine derived boroxazolidones, previously synthesized and reported to have residual activity in a human epithelial cell line, have been evaluated in vitro for their anti-glioblastoma activity. A boroxazolidone derivative containing 2,4-difluorophenyl moieties (6) was found to have higher cytotoxicity than the standard drug, Temozolomide (TMZ). Compound 6 was found to exhibit dose-dependent growth inhibitory effects with an IC₅₀ of 49 μM and 53 μM for LN229 and SNB19 cells, respectively. Additionally, 6 was assessed for its role in apoptosis, caspase 3/7 activation and oxidative stress in SNB19 and LN229 cells. SNB19 cells treated with 6 showed 45.3% apoptosis in the population, while TMZ had 24.7%. In LN229 cells, the percentage of apoptotic cells treated with compound 6 and TMZ were the same. Both 6 and TMZ induced apoptosis through the activation of caspase 3/7 in SNB19 and LN229 cells. Interestingly, 6 exhibited a higher effectivity in promoting reactive oxygen species production in LN229, while it was 6-fold less in SNB19. Boroxazolidone-treated GBM cell lines increased reactive oxygen species production, suggesting that such species may be interlinked with the observed programmed cell death. Additionally, the treatment of both GBM cell lines with 6 led to G2/M phase arrest. The magnitude of anti-GBM effect of 6 is significantly higher than the known chemotherapeutic agent TMZ. This work further demonstrates the anticancer properties of L-valine derived boroxazolidones, adding another potential derivative to the collection of promising chemotherapeutic agents for GBM treatment.

Systems Pharmacogenomic Landscape of Drug Similarities from LINCS data: Drug Association Networks

Modern research in the biomedical sciences is data-driven utilizing high-throughput technologies to generate big genomic data. The Library of Integrated Network-based Cellular Signatures (LINCS) is an example for a large-scale genomic data repository providing hundred thousands of high-dimensional gene expression measurements for thousands of drugs and dozens of cell lines. However, the remaining challenge is how to use these data effectively for pharmacogenomics. In this paper, we use LINCS data to construct drug association networks (DANs) representing the relationships between drugs. By using the Anatomical Therapeutic Chemical (ATC) classification of drugs we demonstrate that the DANs represent a systems pharmacogenomic landscape of drugs summarizing the entire LINCS repository on a genomic scale meaningfully. Here we identify the modules of the DANs as therapeutic attractors of the ATC drug classes.

Alkylaminophenol Induces G1/S Phase Cell Cycle Arrest in Glioblastoma Cells Through p53 and Cyclin-Dependent Kinase Signaling Pathway

Glioblastoma (GBM) is the most common type of malignant brain tumor in adults. We show here that small molecule 2-[(3,4-dihydroquinolin-1(2H)-yl)(p-tolyl)methyl]phenol (THTMP), a potential anticancer agent, increases the human glioblastoma cell death. Its mechanism of action and the interaction of selective signaling pathways remain elusive. Three structurally related phenolic compounds were tested in multiple glioma cell lines in which the potential activity of the compound, THTMP, was further validated and characterized. Upon prolonged exposer to THTMP, all glioma cell lines undergo p53 and cyclin-dependent kinase mediated cell death with the IC₅₀ concentration of 26.5 and 75.4 μM in LN229 and Snb19, respectively. We found that THTMP strongly inhibited cell growth in a dose and in time dependent manner. THTMP treatment led to G1/S cell cycle arrest and apoptosis induction of glioma cell lines. Furthermore, we identified 3,714 genes with significant changes at the transcriptional level in response to THTMP. Further, a transcriptional analysis (RNA-seq) revealed that THTMP targeted the p53 signaling pathway specific genes causing DNA damage and cell cycle arrest at G1/S phase explained by the decrease of cyclin-dependent kinase 1, cyclin A2, cyclin E1 and E2 in glioma cells. Consistently, THTMP induced the apoptosis by regulating the expression of Bcl-2 family genes and reactive oxygen species while it also changed the expression of several anti-apoptotic genes. These observations suggest that THTMP exerts proliferation activity inhibition and pro-apoptosis effects in glioma through affecting cell cycle arrest and intrinsic apoptosis signaling. Importantly, THTMP has more potential at inhibiting GBM cell proliferation compared to TMZ, the current chemotherapy treatment administered to GBM patients; thus, we propose that THTMP may be an alternative therapeutic option for glioblastoma.

Decane-1,2-diol derivatives as potential antitumor agents for the treatment of glioblastoma

Glioblastoma remains the most common and aggressive type of malignant brain tumor among adults thus, considerable attention has been given to discovery of novel anti-tumor drugs for its treatment. This study reports the synthesis of a series of twelve novel decane-1,2-diol derivatives and evaluation of its anti-tumor activity in mammalian glioblastoma cell lines, U87 and LN229. Starting from decane-1,2-diol, several derivatives were prepared using a diversity oriented synthesis approach through which a small library composed of esters, silyl ethers, sulfonates, sulfites, sulfates, ketals, and phosphonates was built. The decane-1,2-diol ditosylated derivative, DBT, found to have higher cytotoxicity than the standard drug cisplatin, has IC₅₀ value of 52 µM in U87 and 270 µM in LN229. Migration analysis of U87 cell line treated with the DBT indicated its ability to effectively suppress proliferation during initial hours of treatment and decrease anti-proliferative property over time. Additionally, DBT was assessed for its role in apoptosis, oxidative stress and caspase 3/7 activation in U87. Interestingly, our experiments indicated that its cytotoxicity is independent of Reactive oxygen species induced caspase 3/7 activity. The compound also exhibited caspase independent apoptosis activity in U87. DBT treatment led to G1/S cell cycle arrest and apoptosis induction of glioma cell lines. In addition, we identified 1533 genes with significant changes at the transcriptional level, in response to DBT. A molecular docking study accounting for the interaction of DBT with NMDA receptor disclosed several hydrogen bonds and charged residue interactions with 17 amino acids, which might be the basis of the DBT cytotoxicity observed. We conclude that this molecule exerts its cytotoxicity via caspase 3/7 independent pathways in glioblastoma cells. Concisely, simple decane-1,2-diol derivatives might serve as scaffolds for the development of effective anti-glioblastoma agents.

Strong FGFR3 staining is a marker for FGFR3 fusions in diffuse gliomas

Background

Inhibitors of fibroblast growth factor receptors (FGFRs) have recently arisen as a promising treatment option for patients with FGFR alterations. Gene fusions involving FGFR3 and transforming acidic coiled-coil protein 3 (TACC3) have been detected in diffuse gliomas and other malignancies, and fusion-positive cases have responded well to FGFR inhibition. As high FGFR3 expression has been detected in fusion-positive tumors, we sought to determine the clinical significance of FGFR3 protein expression level as well as its potential for indicating FGFR3 fusions.

Methods

We performed FGFR3 immunohistochemistry on tissue microarrays containing 676 grades II-IV astrocytomas and 116 grades II-III oligodendroglial tumor specimens. Fifty-one cases were further analyzed using targeted sequencing.

Results

Moderate to strong FGFR3 staining was detected in gliomas of all grades, was more common in females, and was associated with poor survival in diffuse astrocytomas. Targeted sequencing identified FGFR3-TACC3 fusions and an FGFR3-CAMK2A fusion in 10 of 15 strongly stained cases, whereas no fusions were found in 36 negatively to moderately stained cases. Fusion-positive cases were predominantly female and negative for IDH and EGFR/PDGFRA/MET alterations. These and moderately stained cases show lower MIB-1 proliferation index than negatively to weakly stained cases. Furthermore, stronger FGFR3 expression was commonly observed in malignant tissue regions of lower cellularity in fusion-negative cases. Importantly, subregional negative FGFR3 staining was also observed in a few fusion-positive cases.

Conclusions

Strong FGFR3 protein expression is indicative of FGFR3 fusions and may serve as a clinically applicable predictive marker for treatment regimens based on FGFR inhibitors.

Abstract 3427: Gatekeeper inactivation drives glioma progression into secondary glioblastoma

<Glioblastoma (GBM) is the most common and lethal form of brain cancer in humans. Median survival is 15 months with best available treatment. Most GBMs arise de novo (primary GBM), but 5 - 10% progress from lower grade gliomas (secondary GBM). As progression of low grade glioma into secondary GBM significantly impacts prognosis, a better understanding of this process is paramount for treatment and monitoring of affected patients. In this study, we applied whole genome and transcriptome sequencing to primary glioma and relapsed secondary GBM tissue from seven patients with progression. All primary gliomas carried IDH1 mutations, and in all cases the mutation was inherited by the secondary GBM. ATRX alterations in all five astrocytomas and TERT promoter mutations in both 1p19q-codeleted oligoastrocytomas were also inherited in progressed tumors. In five patients, progression was associated with increased genomic instability, whereas mutation load was significantly increased in two other patients. One of them exhibited a hypermutation signature caused by a mutation in the proofreading domain of DNA polymerase epsilon, while the second had lost both copies of the DNA mismatch protein MSH2. In addition, both 1p19q-codeleted tumors had acquired focal inactivating deletions of the protein tyrosine phosphatase PTPRD at progression, suggesting a novel driver mechanism for GBM progression. The most common progression-related genomic alterations were CDKN2A deletions, TP53 mutations, RB1 deletions, PTEN deletions, and deletions of genes crucial to the double strand break repair pathway. Taken together, progression into secondary GBM was significantly related to deletions in tumor suppressor genes as well as TP53 mutations. Disruption of these gatekeepers appears to be a significant mechanism for glioma progression.>

Citation Format: Kirsi Johanna Granberg, Matti Annala, Serafiina Jaatinen, Joonas Haapasalo, Olli Yli-Harja, Hannu Haapasalo, Wei Zhang, Matti Nykter. Gatekeeper inactivation drives glioma progression into secondary glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3427.

Anticancer activity study of A3 adenosine receptor agonists

AIMS

A₃ adenosine receptor (A₃AR) signalling activation seems to mediate anticancer effect, and it has been targeted for drug development. The identification of potent and selective A₃AR agonists could be crucial for cancer drug development.

MATERIALS AND METHODS

In the present study was determined the in vitro activity of known 1-3 and newly 4-6 synthesized compounds with high A₃AR affinity and selectivity (K_i in the low nanomolar range) in binding studies. Effect of known and novel A₃AR agonists on human prostate cancer (PC3), hepatocellular carcinoma (Hep G2), and epithelial colorectal carcinoma (Caco-2) cells were analysed by cytotoxicity assay, dose and time dependent inhibitor assay, migration, apoptosis, autophagy and reactive oxygen species (ROS) assays.

KEY FINDINGS

Results show that the anticancer effect is not due to A₃AR activation alone. In fact, the more active and selective agonist versus A₃AR, compound 1, results inactive on cancer cells such as compounds 2-4. Moreover, results show that the novel compound 5, at micromolar concentration range (IC₅₀ = 28.0 μM), inhibits the growth of PC3, Hep G2, and Caco-2 cells and their migration in time- and dose- dependent manner. The mechanism involved in cell death is attributable to apoptosis. At the same time compound 5 promotes autophagy, which induce apoptosis producing autophagic cell death. Further investigation revealed that compound 5 elevates the level of ROS in all cancer cells tested, suggesting the involvement of ROS in cell death.

SIGNIFICANCE

These results show that the new compound 5 exerts inhibitory effect on cancer cells through differential effect and may serve as a potential anticancer agent.

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.

In vitro characterization of arylhydrazones of active methylene derivatives

Arylhydrazones of active methylene compounds (AHAMCs) are potent chemotherapy agents for the cancer treatment. AHAMCs enhance the apoptotic cell death and antiproliferation properties in cancer cells. In this study, a series of AHAMCs, 13 compounds, was assayed for cytotoxicity, apoptosis, externalization of phosphatidylserine, heterogeneity and cellular calcium level changes. The in vitro cytotoxicity study against HEK293T cells suggests that AHAMCs have significant cytotoxic effect over the concentrations. Top 5 compounds, 5-(2-(2-hydroxyphenyl) hydrazono)pyrimidine-2,4,6(1H,3H,5H)-trione (5), 4-hydroxy-5-(2-(2,4,6-trioxo-tetrahydro-pyrimidin-5(6H) ylidene)hydrazinyl)benzene-1,3-disulfonic acid (6), 5-chloro-3-(2-(4,4-dimethyl-2,6-dioxocyclohexylidene)hydrazinyl)-2-hydroxybenzenesulfonic acid (8), 5-(2-(4,4-dimethyl-2,6-dioxocyclohexylidene)hydrazinyl)-4-hydroxybenzene-1,3-disulfonic acid (9) and 2-(2-sulfophenylhydrazo)malononitrile (10) were chosen for the pharmacodynamics study. Among these, compound 5 exhibited the better cytotoxic effect with the IC₅₀ of 50.86 ± 2.5 mM. DNA cleavage study revealed that 5 induces cell death through apoptosis and shows more effects after 24 and/or 48 h. Independent validation of apoptosis by following the externalization of phosphatidylserine using Annexin-V is also in agreement with the potential activity of 5. Single cell image analysis of Annexin-V bound cells confirms the presence of mixture of early, mid and late apoptotic cells in the population of the cells treated with 5 and a decreased trend in cell-to-cell variation over the phase was also identified. Additionally, intracellular calcium level measurements identified the Ca²⁺ up-regulation in compound treated cells. A brief inspection of the effect of the compound 5 against multiple human brain astrocytoma cells showed a better cell growth inhibitory effect at micro molar level. These systematic studies provide insights in the development of novel AHAMACs compounds as potential cell growth inhibitors for cancer treatment.

In vitro characterization of alkylaminophenols-induced cell death

Alkylaminophenols are synthetic derivatives well known for their anticancer activity. In the previous studies, we described the activity of the series of Alkylaminophenols derivatives and their ability to induce cell death for many cancer cell lines. However, temporal heterogeneity in cell death induced by lead compounds, N-(2-hydroxy-5-nitrophenyl (4'-methylphenyl) methyl) indoline (Compound I) and 2-((3,4-dihydroquinolin-1(2H)-yl) (4-methoxyphenyl) methyl) phenol (Compound II), has never been tested on osteosarcoma cells (U2OS). Here, we address the level of cell-to-cell heterogeneity by examine whether differences in the type of compounds could influence its effects on cell death of U2OS. Here, we applied imaging, computational methods and biochemical methods to study heterogeneity, apoptosis, reactive oxygen species and caspase. Our results demonstrate that the Hill coefficient of dose-response curve of Compound II is greater than compound I in treated U2OS cells. Both Compounds trigger not only apoptotic cell death but also necro-apoptotic and necrotic cell death. The percentage of these sub-populations varies depending on compounds in which greater variance is induced by compound II than Compound I. We also identified the accumulation of compounds-induced reactive oxygen species during the treatment. This resulted in caspase 3/7 activation in turn induced apoptosis. In summary, the screening of Compound I and II molecules for heterogeneity, apoptosis, reactive oxygen species and caspase has identified compound II as promising anti-osteosarcoma cancer agent. Compound II could be a promising lead compound for future antitumor agent development.

Identification of novel GPR17-agonists by structural bioinformatics and signaling activation

G Protein-coupled Receptor 17 (GPR17) is phylogenetically related to the purinergic receptors emerged as a potential drug target for multiple sclerosis, Parkinson disease, Alzheimer disease and cancer. Unfortunately, the crystal structure of GPR17 is unresolved. With the interest in structure-based ligand discovery, we modeled the structure of GPR17. The model allowed us to identify two novel agonists, AC1MLNKK and T0510.3657 that selectively activate GPR17 which exhibit better interaction properties than previously known ligand, MDL29951. We report detailed protein-ligand interactions and the dynamics of GPR17-ligand interaction by molecular docking and molecular dynamics experiments. Ex vivo validation of GPR17-ligand interaction provides evidence that ligand T0510-3657 and AC1MLNKK inhibit the cAMP levels in GPR17-HEK293T cells, with a pEC₅₀ of 4.79 and 4.64, respectively. In silico and ex vivo validation experiments provided the deep understanding of ligand binding with GPR17 and the present findings reported here may lead to use these two compounds as a potential activator of GPR17 for therapeutic intervention.

Effect of alkylaminophenols on growth inhibition and apoptosis of bone cancer cells

In this work, we report the anticancer properties of a series of 11 chemically synthesized alkylaminophenols against human osteosarcoma U2OS tumor cell line. Several assays including cytotoxicity, inhibitor kinetic study, cell migration, Annexin-V/PI double staining, reactive oxygen species (ROS) and caspase 3/7 assays were conducted on this cell line. Cytotoxic 2-((3,4-dihydroquinolin-1(2H)-yl)(p-tolyl)methyl)phenol was determined to have an IC₅₀ value of 36.6μM against U2OS cells and it also inhibits the cell growth in time-dependent manner. The potent activity of lead compound against the growth of multiple cell lines, U2OS, MG-65 and HEK-293T, confirms the osteosarcoma cell specific inhibition. Further studies indicated that such compound is an inhibitor of metastatic property of tumor cells and inducing apoptosis agent. The ability of increasing ROS and inducing caspases 3 and 7 further confirm the contribution of programmed cell death in U2OS and HEK-293T cells. Additionally, four compounds based on the 2-(indolin-1-yl(aryl)methyl)-4-nitrophenol core were also identified to be cytotoxic with IC₅₀ values in the 66-88μM range. This work further demonstrates the anticancer properties of phenol derivatives, adding one more entry to the collection of promising chemotherapeutic agents for cancer treatment.

sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters

Background

sgnesR (Stochastic Gene Network Expression Simulator in R) is an R package that provides an interface to simulate gene expression data from a given gene network using the stochastic simulation algorithm (SSA). The package allows various options for delay parameters and can easily included in reactions for promoter delay, RNA delay and Protein delay. A user can tune these parameters to model various types of reactions within a cell. As examples, we present two network models to generate expression profiles. We also demonstrated the inference of networks and the evaluation of association measure of edge and non-edge components from the generated expression profiles.

Results

The purpose of sgnesR is to enable an easy to use and a quick implementation for generating realistic gene expression data from biologically relevant networks that can be user selected.

Conclusions

sgnesR is freely available for academic use. The R package has been tested for R 3.2.0 under Linux, Windows and Mac OS X.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1731-8) contains supplementary material, which is available to authorized users.