80 research outputs found
Inhibition of prostate cancer cell line (PC-3) by anhydrodihydroartemisinin (ADHA) through caspase-dependent pathway
Cancer is a generic term for a large group of diseases characterized by the growth of abnormal cells, which is the second leading cause of death globally. To treat cancer, currently, a number of anticancer drugs belonging to various classes chemically are available. The discovery of artemisinin and its derivatives such as artesunate, arteether, and artemether became a milestone in the cure for malaria. Here, we report the anti-cancer property of anhydrodihydroartemisinin (ADHA) - a semisynthetic derivative of artemisinin against prostate cancer cell line PC-3. ADHA was found to be inhibiting growth of PC-3 cells. ADHA was also found to be inhibiting migration of PC-3 cells. At molecular level, ADHA was found to be inhibiting the expression of c-Jun, p-c-Jun, p-Akt and NF-ÎşB and activated caspase 3 and 7. The results show that ADHA like few other artemisinin derivatives hold potential to be used as an anti-cancer agent against prostate cancer cells
In-cell NMR characterization of the secondary structure populations of a disordered conformation of α-Synuclein within E. coli cells
α-Synuclein is a small protein strongly implicated in the pathogenesis of Parkinson’s disease and related neurodegenerative disorders. We report here the use of in-cell NMR spectroscopy to observe directly the structure and dynamics of this protein within E. coli cells. To improve the accuracy in the measurement of backbone chemical shifts within crowded in-cell NMR spectra, we have developed a deconvolution method to reduce inhomogeneous line broadening within cellular samples. The resulting chemical shift values were then used to evaluate the distribution of secondary structure populations which, in the absence of stable tertiary contacts, are a most effective way to describe the conformational fluctuations of disordered proteins. The results indicate that, at least within the bacterial cytosol, α-synuclein populates a highly dynamic state that, despite the highly crowded environment, has the same characteristics as the disordered monomeric form observed in aqueous solution
Image informatics strategies for deciphering neuronal network connectivity
Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies
89 Bridging Cell Biology and Engineering Sciences: Interdisciplinary Team-based Training in Computational Pathology – ERRATUM
Collection of Epithelial Cells from Rodent Mammary Gland Via Laser Capture Microdissection Yielding High-Quality RNA Suitable for Microarray Analysis
Laser capture microdissection (LCM) enables collection of cell populations highly enriched for specific cell types that have the potential of yielding critical information about physiological and pathophysiological processes. One use of cells collected by LCM is for gene expression profiling. Samples intended for transcript analyses should be of the highest quality possible. RNA degradation is an ever-present concern in molecular biological assays, and LCM is no exception. This paper identifies issues related to preparation, collection, and processing in a lipid-rich tissue, rodent mammary gland, in which the epithelial to stromal cell ratio is low and the stromal component is primarily adipocytes, a situation that presents numerous technical challenges for high-quality RNA isolation. Our goal was to improve the procedure so that a greater probe set present call rate would be obtained when isolated RNA was evaluated using Affymetrix microarrays. The results showed that the quality of RNA isolated from epithelial cells of both mammary gland and mammary adenocarcinomas was high with a probe set present call rate of 65% and a high signal-to-noise ratio
Optical Drug Monitoring: Photoacoustic Imaging of Nanosensors to Monitor Therapeutic Lithium in Vivo
Personalized medicine could revolutionize how primary care physicians treat chronic disease and how researchers study fundamental biological questions. To realize this goal, we need to develop more robust, modular tools and imaging approaches for in vivo monitoring of analytes. In this report, we demonstrate that synthetic nanosensors can measure physiologic parameters with photoacoustic contrast, and we apply that platform to continuously track lithium levels in vivo. Photoacoustic imaging achieves imaging depths that are unattainable with fluorescence or multiphoton microscopy. We validated the photoacoustic results that illustrate the superior imaging depth and quality of photoacoustic imaging with optical measurements. This powerful combination of techniques will unlock the ability to measure analyte changes in deep tissue and will open up photoacoustic imaging as a diagnostic tool for continuous physiological tracking of a wide range of analytes
Die Attraktivität großer Städte - ökonomisch, demografisch, kulturell: Ergebnisse eines Ressortforschungsprojekts des Bundes
In einer Reihe deutscher Großstädte zeichnet sich seit Jahren eine positive Bevölkerungsentwicklung ab, während Suburbanisierungstendenzen rückläufig sind. Diese Entwicklung hat in Fachkreisen eine umfassende Re-Urbanisierungsdebatte ausgelöst. Damit verbunden die Hoffnung, das planerische Ideal von Urbanität, kompakter Stadtentwicklung sowie von weniger Pendlerverkehr und Zersiedelung werde sich nun endlich umsetzen lassen. Wie schätzen die Städte selber diesen Trend, seine Ursachen und Folgen ein? Wer wandert in die Städte und in welche Stadtquartiere? Wie geht die Stadtplanung mit dem Wachstum um, was hat sie dazu beigetragen? In einem Ressortforschungsprojekt des Bundesministeriums für Verkehr, Bau und Stadtentwicklung (BMVBS) hat das Bundesinstitut für Bau-, Stadt- und Raumforschung (BBSR) dieses Thema zusammen mit Vertreterinnen und Vertretern aus der Verwaltung deutscher Großstädte behandelt, die in den letzten Jahren ein kontinuierliches Wachstum ihrer Bevölkerungszahlen zu verzeichnen hatten - darunter München, Bonn, Jena und Ingolstadt. Die Publikation lenkt den Blick ganz konkret auf einige der wachsenden Städte, die an dem Projekt beteiligt waren, auf die Ursachen sowie stadtentwicklungspolitischen Implikationen ihres Wachstums. Expertenbeiträge greifen zudem aktuelle Aspekte der Entwicklung der Städte und des Städtischen auf
Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size
Abstract Background Co-expression has been widely used to identify novel regulatory relationships using high throughput measurements, such as microarray and RNA-seq data. Evaluation studies on co-expression network analysis methods mostly focus on networks of small or medium size of up to a few hundred nodes. For large networks, simulated expression data usually consist of hundreds or thousands of profiles with different perturbations or knock-outs, which is uncommon in real experiments due to their cost and the amount of work required. Thus, the performances of co-expression network analysis methods on large co-expression networks consisting of a few thousand nodes, with only a small number of profiles with a single perturbation, which more accurately reflect normal experimental conditions, are generally uncharacterized and unknown. Methods We proposed a novel network inference methods based on Relevance Low order Partial Correlation (RLowPC). RLowPC method uses a two-step approach to select on the high-confidence edges first by reducing the search space by only picking the top ranked genes from an intial partial correlation analysis and, then computes the partial correlations in the confined search space by only removing the linear dependencies from the shared neighbours, largely ignoring the genes showing lower association. Results We selected six co-expression-based methods with good performance in evaluation studies from the literature: Partial correlation, PCIT, ARACNE, MRNET, MRNETB and CLR. The evaluation of these methods was carried out on simulated time-series data with various network sizes ranging from 100 to 3000 nodes. Simulation results show low precision and recall for all of the above methods for large networks with a small number of expression profiles. We improved the inference significantly by refinement of the top weighted edges in the pre-inferred partial correlation networks using RLowPC. We found improved performance by partitioning large networks into smaller co-expressed modules when assessing the method performance within these modules. Conclusions The evaluation results show that current methods suffer from low precision and recall for large co-expression networks where only a small number of profiles are available. The proposed RLowPC method effectively reduces the indirect edges predicted as regulatory relationships and increases the precision of top ranked predictions. Partitioning large networks into smaller highly co-expressed modules also helps to improve the performance of network inference methods. The RLowPC R package for network construction, refinement and evaluation is available at GitHub: https://github.com/wyguo/RLowPC
Visualizing Escherichia coli Sub-Cellular Structure Using Sparse Deconvolution Spatial Light Interference Tomography
Studying the 3D sub-cellular structure of living cells is essential to our understanding of biological function. However, tomographic imaging of live cells is challenging mainly because they are transparent, i.e., weakly scattering structures. Therefore, this type of imaging has been implemented largely using fluorescence techniques. While confocal fluorescence imaging is a common approach to achieve sectioning, it requires fluorescence probes that are often harmful to the living specimen. On the other hand, by using the intrinsic contrast of the structures it is possible to study living cells in a non-invasive manner. One method that provides high-resolution quantitative information about nanoscale structures is a broadband interferometric technique known as Spatial Light Interference Microscopy (SLIM). In addition to rendering quantitative phase information, when combined with a high numerical aperture objective, SLIM also provides excellent depth sectioning capabilities. However, like in all linear optical systems, SLIM's resolution is limited by diffraction. Here we present a novel 3D field deconvolution algorithm that exploits the sparsity of phase images and renders images with resolution beyond the diffraction limit. We employ this label-free method, called deconvolution Spatial Light Interference Tomography (dSLIT), to visualize coiled sub-cellular structures in E. coli cells which are most likely the cytoskeletal MreB protein and the division site regulating MinCDE proteins. Previously these structures have only been observed using specialized strains and plasmids and fluorescence techniques. Our results indicate that dSLIT can be employed to study such structures in a practical and non-invasive manner
A high quality assembly of the Nile Tilapia (Oreochromis niloticus) genome reveals the structure of two sex determination regions
Background Tilapias are the second most farmed fishes in the world and a sustainable source of food. Like many other fish, tilapias are sexually dimorphic and sex is a commercially important trait in these fish. In this study, we developed a significantly improved assembly of the tilapia genome using the latest genome sequencing methods and show how it improves the characterization of two sex determination regions in two tilapia species. Results A homozygous clonal XX female Nile tilapia (Oreochromis niloticus) was sequenced to 44X coverage using Pacific Biosciences (PacBio) SMRT sequencing. Dozens of candidate de novo assemblies were generated and an optimal assembly (contig NG50 of 3.3Mbp) was selected using principal component analysis of likelihood scores calculated from several paired-end sequencing libraries. Comparison of the new assembly to the previous O. niloticus genome assembly reveals that recently duplicated portions of the genome are now well represented. The overall number of genes in the new assembly increased by 27.3%, including a 67% increase in pseudogenes. The new tilapia genome assembly correctly represents two recentvasagene duplication events that have been verified with BAC sequencing. At total of 146Mbp of additional transposable element sequence are now assembled, a large proportion of which are recent insertions. Large centromeric satellite repeats are assembled and annotated in cichlid fish for the first time. Finally, the new assembly identifies the long-range structure of both a ~9Mbp XY sex determination region on LG1 in O. niloticus, and a ~50Mbp WZ sex determination region on LG3 in the related species O. aureus. Conclusions This study highlights the use of long read sequencing to correctly assemble recent duplications and to characterize repeat-filled regions of the genome. The study serves as an example of the need for high quality genome assemblies and provides a framework for identifying sex determining genes in tilapia and related fish species
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