Structural analysis of human G-protein-coupled receptor 17 ligand binding sites.

The human G protein coupled membrane receptor (GPR17), the sensor of brain damage, is identified as a biomarker for many neurological diseases. In human brain tissue, GPR17 exist in two isoforms, long and short. While cryo-electron microscopy technology has provided the structure of the long isoform of GPR17 with Gi complex, the structure of the short isoform and its activation mechanism remains unclear. Recently, we theoretically modeled the structure of the short isoform of GPR17 with Gi signaling protein and identified novel ligands. In the present work, we demonstrated the presence of two distinct ligand binding sites in the short isoform of GPR17. The molecular docking of GPR17 with endogenous (UDP) and synthetic ligands (T0510.3657, MDL29950) found the presence of two distinct binding pockets. Our observations revealed that endogenous ligand UDP can bind stronger in two different binding pockets as evidenced by glide and autodock vina scores, whereas the other two ligand's binding with GPR17 has less docking score. The analysis of receptor-UDP interactions shows complexes' stability in the lipid environment by 100 ns atomic molecular dynamics simulations. The amino acid residues VAL83, ARG87, and PHE111 constitute ligand binding site 1, whereas site 2 constitutes ASN67, ARG129, and LYS232. Root mean square fluctuation analysis showed the residues 83, 87, and 232 with higher fluctuations during molecular dynamics simulation in both binding pockets. Our findings imply that the residues of GPR17's two binding sites are crucial, and their interaction with UDP reveals the protein's hidden signaling and communication properties. Furthermore, this finding may assist in the development of targeted therapies for the treatment of neurological diseases.

Programmed cell death detection methods: a systematic review and a categorical comparison.

Programmed cell death is considered a key player in a variety of cellular processes that helps to regulate tissue growth, embryogenesis, cell turnover, immune response, and other biological processes. Among different types of cell death, apoptosis has been studied widely, especially in the field of cancer research to understand and analyse cellular mechanisms, and signaling pathways that control cell cycle arrest. Hallmarks of different types of cell death have been identified by following the patterns and events through microscopy. Identified biomarkers have also supported drug development to induce cell death in cancerous cells. There are various serological and microscopic techniques with advantages and limitations, that are available and are being utilized to detect and study the mechanism of cell death. The complexity of the mechanism and difficulties in distinguishing among different types of programmed cell death make it challenging to carry out the interventions and delay its progression. In this review, mechanisms of different forms of programmed cell death along with their conventional and unconventional methods of detection of have been critically reviewed systematically and categorized on the basis of morphological hallmarks and biomarkers to understand the principle, mechanism, application, advantages and disadvantages of each method. Furthermore, a very comprehensive comparative analysis has been drawn to highlight the most efficient and effective methods of detection of programmed cell death, helping researchers to make a reliable and prudent selection among the available methods of cell death assay. Conclusively, how programmed cell death detection methods can be improved and can provide information about distinctive stages of cell death detection have been discussed.

P2Y1 agonist HIC in combination with androgen receptor inhibitor abiraterone acetate impairs cell growth of prostate cancer.

P2Y receptors belong to the large superfamily of G-protein-coupled receptors and play a crucial role in cell death and survival. P2Y1 receptor has been identified as a marker for prostate cancer (PCa). A previously unveiled selective P2Y1 receptor agonist, the indoline-derived HIC (1-(1-((2-hydroxy-5-nitrophenyl)(4-hydroxyphenyl)methyl)indoline-4-carbonitrile), induces a series of molecular and biological responses in PCa cells PC3 and DU145, but minimal toxicity to normal cells. Here, we evaluated the combinatorial effect of HIC with abiraterone acetate (AA) targeted on androgen receptor (AR) on the inhibition of PCa cells. Here, the presence of HIC and AA significantly inhibited cell proliferation of PC3 and DU145 cells with time-dependent manner as a synerfistic combination. Moreover, it was also shown that the anticancer and antimetastasis effects of the combinratorial drugs were noticed through a decrease in colony-forming ability, cell migration, and cell invasion. In addition, the HIC + AA induced apoptotic population of PCa cells as well as cell cycle arrest in G1 progression phase. In summary, these studies show that the combination of P2Y1 receptor agonist, HIC and AR inhibitor, AA, effectively improved the antitumor activity of each drug. Thus, the combinatorial model of HIC and AA should be a novel and promising therapeutic strategy for treating prostate cancer.

What Is a Digital Twin? Experimental Design for a Data-Centric Machine Learning Perspective in Health.

The idea of a digital twin has recently gained widespread attention. While, so far, it has been used predominantly for problems in engineering and manufacturing, it is believed that a digital twin also holds great promise for applications in medicine and health. However, a problem that severely hampers progress in these fields is the lack of a solid definition of the concept behind a digital twin that would be directly amenable for such big data-driven fields requiring a statistical data analysis. In this paper, we address this problem. We will see that the term 'digital twin', as used in the literature, is like a Matryoshka doll. For this reason, we unstack the concept via a data-centric machine learning perspective, allowing us to define its main components. As a consequence, we suggest to use the term Digital Twin System instead of digital twin because this highlights its complex interconnected substructure. In addition, we address ethical concerns that result from treatment suggestions for patients based on simulated data and a possible lack of explainability of the underling models.

Identifying the miRNA Signature Association with Aging-Related Senescence in Glioblastoma.

Glioblastoma (GBM) is the most common malignant brain tumor and its malignant phenotypic characteristics are classified as grade IV tumors. Molecular interactions, such as protein-protein, protein-ncRNA, and protein-peptide interactions are crucial to transfer the signaling communications in cellular signaling pathways. Evidences suggest that signaling pathways of stem cells are also activated, which helps the propagation of GBM. Hence, it is important to identify a common signaling pathway that could be visible from multiple GBM gene expression data. microRNA signaling is considered important in GBM signaling, which needs further validation. We performed a high-throughput analysis using micro array expression profiles from 574 samples to explore the role of non-coding RNAs in the disease progression and unique signaling communication in GBM. A series of computational methods involving miRNA expression, gene ontology (GO) based gene enrichment, pathway mapping, and annotation from metabolic pathways databases, and network analysis were used for the analysis. Our study revealed the physiological roles of many known and novel miRNAs in cancer signaling, especially concerning signaling in cancer progression and proliferation. Overall, the results revealed a strong connection with stress induced senescence, significant miRNA targets for cell cycle arrest, and many common signaling pathways to GBM in the network.

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.

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.

Genomically amplified Akt3 activates DNA repair pathway and promotes glioma progression.

Akt is a robust oncogene that plays key roles in the development and progression of many cancers, including glioma. We evaluated the differential propensities of the Akt isoforms toward progression in the well-characterized RCAS/Ntv-a mouse model of PDGFB-driven low grade glioma. A constitutively active myristoylated form of Akt1 did not induce high-grade glioma (HGG). In stark contrast, Akt2 and Akt3 showed strong progression potential with 78% and 97% of tumors diagnosed as HGG, respectively. We further revealed that significant variations in polarity and hydropathy values among the Akt isoforms in both the pleckstrin homology domain (P domain) and regulatory domain (R domain) were critical in mediating glioma progression. Gene expression profiles from representative Akt-derived tumors indicated dominant and distinct roles for Akt3, consisting primarily of DNA repair pathways. TCGA data from human GBM closely reflected the DNA repair function, as Akt3 was significantly correlated with a 76-gene signature DNA repair panel. Consistently, compared with Akt1 and Akt2 overexpression models, Akt3-expressing human GBM cells had enhanced activation of DNA repair proteins, leading to increased DNA repair and subsequent resistance to radiation and temozolomide. Given the wide range of Akt3-amplified cancers, Akt3 may represent a key resistance factor.

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 ±â€¯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.

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.

Clinical association analysis of ependymomas and pilocytic astrocytomas reveals elevated FGFR3 and FGFR1 expression in aggressive ependymomas.

BACKGROUND: Fibroblast growth factor receptors (FGFRs) are well-known proto-oncogenes in several human malignancies and are currently therapeutically targeted in clinical trials. Among glioma subtypes, activating FGFR1 alterations have been observed in a subpopulation of pilocytic astrocytomas while FGFR3 fusions occur in IDH wild-type diffuse gliomas, resulting in high FGFR3 protein expression. The purpose of this study was to associate FGFR1 and FGFR3 protein levels with clinical features and genetic alterations in ependymoma and pilocytic astrocytoma. METHODS: FGFR1 and FGFR3 expression levels were detected in ependymoma and pilocytic astrocytoma tissues using immunohistochemistry. Selected cases were further analyzed using targeted sequencing. RESULTS: Expression of both FGFR1 and FGFR3 varied within all tumor types. In ependymomas, increased FGFR3 or FGFR1 expression was associated with high tumor grade, cerebral location, young patient age, and poor prognosis. Moderate-to-strong expression of FGFR1 and/or FGFR3 was observed in 76% of cerebral ependymomas. Cases with moderate-to-strong expression of both proteins had poor clinical prognosis. In pilocytic astrocytomas, moderate-to-strong FGFR3 expression was detected predominantly in non-pediatric patients. Targeted sequencing of 12 tumors found no protein-altering mutations or fusions in FGFR1 or FGFR3. CONCLUSIONS: Elevated FGFR3 and FGFR1 protein expression is common in aggressive ependymomas but likely not driven by genetic alterations. Further studies are warranted to evaluate whether ependymoma patients with high FGFR3 and/or FGFR1 expression could benefit from treatment with FGFR inhibitor based therapeutic approaches currently under evaluation in clinical trials.

Transcription closed and open complex dynamics studies reveal balance between genetic determinants and co-factors.

In E. coli, promoter closed and open complexes are key steps in transcription initiation, where magnesium-dependent RNA polymerase catalyzes RNA synthesis. However, the exact mechanism of initiation remains to be fully elucidated. Here, using single mRNA detection and dual reporter studies, we show that increased intracellular magnesium concentration affects Plac initiation complex formation resulting in a highly dynamic process over the cell growth phases. Mg2+ regulates transcription transition, which modulates bimodality of mRNA distribution in the exponential phase. We reveal that Mg2+ regulates the size and frequency of the mRNA burst by changing the open complex duration. Moreover, increasing magnesium concentration leads to higher intrinsic and extrinsic noise in the exponential phase. RNAP-Mg2+ interaction simulation reveals critical movements creating a shorter contact distance between aspartic acid residues and Nucleotide Triphosphate residues and increasing electrostatic charges in the active site. Our findings provide unique biophysical insights into the balanced mechanism of genetic determinants and magnesium ion in transcription initiation regulation during cell growth.

In vivo single-molecule kinetics of activation and subsequent activity of the arabinose promoter.

Using a single-RNA detection technique in live Escherichia coli cells, we measure, for each cell, the waiting time for the production of the first RNA under the control of PBAD promoter after induction by arabinose, and subsequent intervals between transcription events. We find that the kinetics of the arabinose intake system affect mean and diversity in RNA numbers, long after induction. We observed the same effect on Plac/ara-1 promoter, which is inducible by arabinose or by IPTG. Importantly, the distribution of waiting times of Plac/ara-1 is indistinguishable from that of PBAD, if and only if induced by arabinose alone. Finally, RNA production under the control of PBAD is found to be a sub-Poissonian process. We conclude that inducer-dependent waiting times affect mean and cell-to-cell diversity in RNA numbers long after induction, suggesting that intake mechanisms have non-negligible effects on the phenotypic diversity of cell populations in natural, fluctuating environments.

Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling.

We examine the role of big data and machine learning in cancer research. We describe an example in cancer research where gene-level data from The Cancer Genome Atlas (TCGA) consortium is interpreted using a pathway-level model. As the complexity of computational models increases, their sample requirements grow exponentially. This growth stems from the fact that the number of combinations of variables grows exponentially as the number of variables increases. Thus, a large sample size is needed. The number of variables in a computational model can be reduced by incorporating biological knowledge. One particularly successful way of doing this is by using available gene regulatory, signaling, metabolic, or context-specific pathway information. We conclude that the incorporation of existing biological knowledge is essential for the progress in using big data for cancer research.

Robustness of the division symmetry in Escherichia coli and functional consequences of symmetry breaking.

The morphological symmetry of the division process of Escherichia coli is well-known. Recent studies verified that, in optimal growth conditions, most divisions are symmetric, although there are exceptions. We investigate whether such morphological asymmetries in division introduce functional asymmetries between sister cells, and assess the robustness of the symmetry in division to mild chemical stresses and sub-optimal temperatures. First, we show that the difference in size between daughter cells at birth is positively correlated to the difference between the numbers of fluorescent protein complexes inherited from the parent cell. Next, we show that the degree of symmetry in division observed in optimal conditions is robust to mild acidic shift and to mild oxidative stress, but not to sub-optimal temperatures, in that the variance of the difference between the sizes of sister cells at birth is minimized at 37 °C. This increased variance affects the functionality of the cells in that, at sub-optimal temperatures, larger/smaller cells arising from asymmetric divisions exhibit faster/slower division times than the mean population division time, respectively. On the other hand, cells dividing faster do not do so at the cost of morphological symmetry in division. Finally we show that at suboptimal temperatures the mean distance between the nucleoids increases, explaining the increased variance in division. We conclude that the functionality of E. coli cells is not immune to morphological asymmetries at birth, and that the effectiveness of the mechanism responsible for ensuring the symmetry in division weakens at sub-optimal temperatures.

Transcriptome and small RNA deep sequencing reveals deregulation of miRNA biogenesis in human glioma.

Altered expression of oncogenic and tumour-suppressing microRNAs (miRNAs) is widely associated with tumourigenesis. However, the regulatory mechanisms underlying these alterations are poorly understood. We sought to shed light on the deregulation of miRNA biogenesis promoting the aberrant miRNA expression profiles identified in these tumours. Using sequencing technology to perform both whole-transcriptome and small RNA sequencing of glioma patient samples, we examined precursor and mature miRNAs to directly evaluate the miRNA maturation process, and examined expression profiles for genes involved in the major steps of miRNA biogenesis. We found that ratios of mature to precursor forms of a large number of miRNAs increased with the progression from normal brain to low-grade and then to high-grade gliomas. The expression levels of genes involved in each of the three major steps of miRNA biogenesis (nuclear processing, nucleo-cytoplasmic transport, and cytoplasmic processing) were systematically altered in glioma tissues. Survival analysis of an independent data set demonstrated that the alteration of genes involved in miRNA maturation correlates with survival in glioma patients. Direct quantification of miRNA maturation with deep sequencing demonstrated that deregulation of the miRNA biogenesis pathway is a hallmark for glioma genesis and progression.

Dynamics of transcription driven by the tetA promoter, one event at a time, in live Escherichia coli cells.

In Escherichia coli, tetracycline prevents translation. When subject to tetracycline, E. coli express TetA to pump it out by a mechanism that is sensitive, while fairly independent of cellular metabolism. We constructed a target gene, PtetA-mRFP1-96BS, with a 96 MS2-GFP binding site array in a single-copy BAC vector, whose expression is controlled by the tetA promoter. We measured the in vivo kinetics of production of individual RNA molecules of the target gene as a function of inducer concentration and temperature. From the distributions of intervals between transcription events, we find that RNA production by PtetA is a sub-Poissonian process. Next, we infer the number and duration of the prominent sequential steps in transcription initiation by maximum likelihood estimation. Under full induction and at optimal temperature, we observe three major steps. We find that the kinetics of RNA production under the control of PtetA, including number and duration of the steps, varies with induction strength and temperature. The results are supported by a set of logical pairwise Kolmogorov-Smirnov tests. We conclude that the expression of TetA is controlled by a sequential mechanism that is robust, whereas sensitive to external signals.

Aberrant DNA methylation distorts developmental trajectories in atypical teratoid/rhabdoid tumors.

Atypical teratoid/rhabdoid tumors (AT/RTs) are pediatric brain tumors known for their aggressiveness and aberrant but still unresolved epigenetic regulation. To better understand their malignancy, we investigated how AT/RT-specific DNA hypermethylation was associated with gene expression and altered transcription factor binding and how it is linked to upstream regulation. Medulloblastomas, choroid plexus tumors, pluripotent stem cells, and fetal brain were used as references. A part of the genomic regions, which were hypermethylated in AT/RTs similarly as in pluripotent stem cells and demethylated in the fetal brain, were targeted by neural transcriptional regulators. AT/RT-unique DNA hypermethylation was associated with polycomb repressive complex 2 and linked to suppressed genes with a role in neural development and tumorigenesis. Activity of the several NEUROG/NEUROD pioneer factors, which are unable to bind to methylated DNA, was compromised via the suppressed expression or DNA hypermethylation of their target sites, which was also experimentally validated for NEUROD1 in medulloblastomas and AT/RT samples. These results highlight and characterize the role of DNA hypermethylation in AT/RT malignancy and halted neural cell differentiation.

Cell segmentation by multi-resolution analysis and maximum likelihood estimation (MAMLE).

BACKGROUND: Cell imaging is becoming an indispensable tool for cell and molecular biology research. However, most processes studied are stochastic in nature, and require the observation of many cells and events. Ideally, extraction of information from these images ought to rely on automatic methods. Here, we propose a novel segmentation method, MAMLE, for detecting cells within dense clusters. METHODS: MAMLE executes cell segmentation in two stages. The first relies on state of the art filtering technique, edge detection in multi-resolution with morphological operator and threshold decomposition for adaptive thresholding. From this result, a correction procedure is applied that exploits maximum likelihood estimate as an objective function. Also, it acquires morphological features from the initial segmentation for constructing the likelihood parameter, after which the final segmentation is obtained. CONCLUSIONS: We performed an empirical evaluation that includes sample images from different imaging modalities and diverse cell types. The new method attained very high (above 90%) cell segmentation accuracy in all cases. Finally, its accuracy was compared to several existing methods, and in all tests, MAMLE outperformed them in segmentation accuracy.