The design of in vitro models that mimic the stratified multicellular hepatic microenvironment continues to be challenging. Although several in vitro hepatic cultures have been shown to exhibit liver functions, their physiological relevance is limited due to significant deviation from in vivo cellular composition. We report the assembly of a novel three-dimensional (3D) organotypic liver model incorporating three different cell types (hepatocytes, liver sinusoidal endothelial cells, and Kupffer cells) and a polymeric interface that mimics the Space of Disse. The nanoscale interface is detachable, optically transparent, derived from self-assembled polyelectrolyte multilayers, and exhibits a Young's modulus similar to in vivo values for liver tissue. Only the 3D liver models simultaneously maintain hepatic phenotype and elicit proliferation, while achieving cellular ratios found in vivo. The nanoscale detachable polymeric interfaces can be modulated to mimic basement membranes that exhibit a wide range of physical properties. This facile approach offers a versatile new avenue in the assembly of engineered tissues. These results demonstrate the ability of the tri-cellular 3D cultures to serve as an organotypic hepatic model that elicits proliferation and maintenance of phenotype and in vivo-like cellular ratios.
Effectively educating families about the risks and benefits of genomic tests such as whole exome sequencing (WES) offers numerous challenges, including the complexity of test results and potential loss of privacy. Research on best practices for obtaining informed consent (IC) in a variety of clinical settings is needed. The BASIC3 study of clinical tumor and germline WES in an ethnically diverse cohort of newly diagnosed pediatric cancer patients offers the opportunity to study the IC process in the setting of critical illness. We report on our experience for the first 100 families enrolled, including study participation rates, reasons for declining enrollment, assessment of clinical and demographic factors that might impact study enrollment, and preferences of parents for participation in optional genomics study procedures.
A specifically trained IC team offered study enrollment to parents of eligible children for procedures including clinical tumor and germline WES with results deposited in the medical record and disclosure of both diagnostic and incidental results to the family. Optional study procedures were also offered, such as receiving recessive carrier status and deposition of data into research databases. Stated reasons for declining participation were recorded. Clinical and demographic data were collected and comparisons made between enrolled and non-enrolled patients.
Over 15 months, 100 of 121 (83%) eligible families elected to enroll in the study. No significant differences in enrollment were detected based on factors such as race, ethnicity, use of Spanish interpreters and Spanish consent forms, and tumor features (central nervous system versus non-central nervous system, availability of tumor for WES). The most common reason provided for declining enrollment (10% of families) was being overwhelmed by the new cancer diagnosis. Risks specific to clinical genomics, such as privacy concerns, were less commonly reported (5.5%). More than 85% of parents consented to each of the optional study procedures.
An IC process was developed that utilizes a specialized IC team, active communication with the oncology team, and an emphasis on scheduling flexibility. Most parents were willing to participate in a clinical germline and tumor WES study as well as optional procedures such as genomic data sharing independent of race, ethnicity or language spoken.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-014-0069-3) contains supplementary material, which is available to authorized users.
Pancreatic cancer has a poor prognosis due to late diagnosis and ineffective therapeutic multimodality. MUC13, a transmembrane mucin is highly involved in pancreatic cancer progression. Thus, understanding its regulatory molecular mechanisms may offer new avenue of therapy for prevention/treatment of pancreatic cancer. Herein, we report a novel microRNA (miR-145)-mediated mechanism regulating aberrant MUC13 expression in pancreatic cancer. We report that miR-145 expression inversely correlates with MUC13 expression in pancreatic cancer cells and human tumor tissues. miR-145 is predominantly present in normal pancreatic tissues and early Pancreatic Ductal Adenocarcinoma (PDAC) precursor lesions (PanIN I) and is progressively suppressed over the course of development from PanIN II/III to late stage poorly differentiated PDAC. We demonstrate that miR-145 targets 3′ untranslated region of MUC13 and thus downregulates MUC13 protein expression in cells. Interestingly, transfection of miR-145 inhibits cell proliferation, invasion and enhances gemcitabine sensitivity. It causes reduction of HER2, P-AKT, PAK1 and an increase in p53. Similar results were found when MUC13 was specifically inhibited by shRNA directed at MUC13. Additionally, intratumoral injections of miR-145 in xenograft mice inhibited tumor growth via suppression of MUC13 and its downstream target, HER2. These results suggest miR-145 as a novel regulator of MUC13 in pancreatic cancer.
Pancreatic cancer; MUC13; MicroRNA; Tumor suppressor; Diagnostics; Therapeutics
Curcumin (CUR), a naturally occurring polyphenol derived from the root of Curcuma longa, has demonstrated potent anti-cancer and cancer prevention activity in a variety of cancers. However, the clinical translation of curcumin has been significantly hampered due to its extensive degradation, suboptimal pharmacokinetics and poor bioavailability. To address these clinically relevant issues, we have developed a novel curcumin loaded magnetic nanoparticle (MNP-CUR) formulation. Herein, we have evaluated the in vitro and in vivo therapeutic efficacy of this novel MNP-CUR formulation in pancreatic cancer. Human pancreatic cancer cells (HPAF-II and Panc-1) exhibited efficient internalization of the MNP-CUR formulation in a dose dependent manner. As a result, the MNP-CUR formulation effectively inhibited growth of HPAF-II and Panc-1 cells in cell proliferation and colony formation assays. The MNP-CUR formulation suppressed pancreatic tumor growth in an HPAF-II xenograft mice model and improved mice survival by delaying tumor growth. The growth inhibitory effect of MNP-CUR formulation was correlated with the suppression of PCNA, Bcl-xL, Mcl-1, MUC1, Collagen I and enhanced membrane β-catenin expression. MNP-CUR formulation did not show any sign of hemotoxicity and was stable after incubation with human serum proteins. Additionally, the MNP-CUR formulation improved serum bioavailability of curcumin in mice up to 2.5 fold as compared to free curcumin. Biodistribution studies demonstrate that a significant amount of MNP-CUR formulation was able to reach the pancreatic xenograft tumor(s) which suggests its clinical translational potential. In conclusion, this study suggests that our novel MNP-CUR formulation can be valuable for the treatment of pancreatic cancer.
magnetic nanoparticles; curcumin; chemoprevention; pancreatic cancer; nanomedicine
The complexity, diversity, and richness of experimental data on cellular systems are inspiring the development of computational analysis techniques that can directly prioritize and suggest new experiments.
Analysis of molecular interaction networks is pervasive in systems biology. This research relies almost entirely on graphs for modeling interactions. However, edges in graphs cannot represent multiway interactions among molecules, which occur very often within cells. Hypergraphs may be better representations for networks having such interactions, since hyperedges can naturally represent relationships among multiple molecules. Here, we propose using hypergraphs to capture the uncertainty inherent in reverse engineering gene-gene networks. Some subsets of nodes may induce highly varying subgraphs across an ensemble of networks inferred by a reverse engineering algorithm. We provide a novel formulation of hyperedges to capture this uncertainty in network topology. We propose a clustering-based approach to discover hyperedges. We show that our approach can recover hyperedges planted in synthetic data sets with high precision and recall, even for moderate amount of noise. We apply our techniques to a data set of pathways inferred from genetic interaction data in S. cerevisiae related to the unfolded protein response. Our approach discovers several hyperedges that capture the uncertain connectivity of genes in relevant protein complexes, suggesting that further experiments may be required to precisely discern their interaction patterns. We also show that these complexes are not discovered by an algorithm that computes frequent and dense subgraphs.
Biology and genetics; hypergraphs; graphs and networks
Curcumin, a natural bioactive polyphenol, has been widely investigated as a conventional medicine for centuries. Over the past two decades, major pre-clinical and clinical trials have demonstrated its safe therapeutic profile but clinical translation has been hampered due to rapid degradation, poor water solubility, bioavailability and pharmaco-kinetics. To overcome such translational issues, many laboratories have focused on developing curcumin nanoformulations for cancer therapeutics. In this review, we discuss the evolution of curcumin nanomedicine in cancer therapeutics, the possible interactions between the surface of curcumin nanoparticles and plasma proteins, the role of nanoparticle-protein complex architecture parameters, and the rational design of clinically useful curcumin nanoformulations. Considering all the biologically relevant phenomena, curcumin nanoformulations can be developed as a new neutraceutical or pharmaceutical agent.
Polyphenol; drug delivery; nanomedicine; cancer therapeutics; bioavailability; protein corona
Top-down analyses in systems biology can automatically find correlations among genes and proteins in large-scale datasets. However, it is often difficult to design experiments from these results. In contrast, bottom-up approaches painstakingly craft detailed models that can be simulated computationally to suggest wet lab experiments. However, developing the models is a manual process that can take many years. These approaches have largely been developed independently.
We present Linker, an efficient and automated data-driven method that can analyze molecular interactomes to propose extensions to models that can be simulated. Linker combines teleporting random walks and k-shortest path computations to discover connections from a source protein to a set of proteins collectively involved in a particular cellular process.
We evaluate the efficacy of Linker by applying it to a well-known dynamic model of the cell division cycle in Saccharomyces cerevisiae. Compared to other state-of-the-art methods, subnetworks computed by Linker are heavily enriched in Gene Ontology (GO) terms relevant to the cell cycle. Finally, we highlight how networks computed by Linker elucidate the role of a protein kinase (Cdc5) in the mitotic exit network of a dynamic model of the cell cycle.
algorithms; biochemical networks; biology; computational molecular biology; gene expression; graphs and networks; graph theory; pathways
Motivation: Many techniques have been developed to compute the response network of a cell. A recent trend in this area is to compute response networks of small size, with the rationale that only part of a pathway is often changed by disease and that interpreting small subnetworks is easier than interpreting larger ones. However, these methods may not uncover the spectrum of pathways perturbed in a particular experiment or disease.
Results: To avoid these difficulties, we propose to use algorithms that reconcile case-control DNA microarray data with a molecular interaction network by modifying per-gene differential expression P-values such that two genes connected by an interaction show similar changes in their gene expression values. We provide a novel evaluation of four methods from this class of algorithms. We enumerate three desirable properties that this class of algorithms should address. These properties seek to maintain that the returned gene rankings are specific to the condition being studied. Moreover, to ease interpretation, highly ranked genes should participate in coherent network structures and should be functionally enriched with relevant biological pathways. We comprehensively evaluate the extent to which each algorithm addresses these properties on a compendium of gene expression data for 54 diverse human diseases. We show that the reconciled gene rankings can identify novel disease-related functions that are missed by analyzing expression data alone.
Availability: C++ software implementing our algorithms is available in the NetworkReconciliation package as part of the Biorithm software suite under the GNU General Public License: http://bioinformatics.cs.vt.edu/∼murali/software/biorithm-docs.
Supplementary information: Supplementary data are available at Bioinformatics online.
Cancer is the second leading cause of death in the United States. Conventional therapies cause widespread systemic toxicity and lead to serious side effects which prohibit their long term use. Additionally, in many circumstances tumor resistance and recurrence is commonly observed. Therefore, there is an urgent need to identify suitable anticancer therapies that are highly precise with minimal side effects. Curcumin is a natural polyphenol molecule derived from the Curcuma longa plant which exhibits anticancer, chemo-preventive, chemo- and radio-sensitization properties. Curcumin’s widespread availability, safety, low cost and multiple cancer fighting functions justify its development as a drug for cancer treatment. However, various basic and clinical studies elucidate curcumin’s limited efficacy due to its low solubility, high rate of metabolism, poor bioavailability and pharmacokinetics. A growing list of nanomedicine(s) using first line therapeutic drugs have been approved or are under consideration by the Food and Drug Administration (FDA) to improve human health. These nanotechnology strategies may help to overcome challenges and ease the translation of curcumin from bench to clinical application. Prominent research is reviewed which shows that advanced drug delivery of curcumin (curcumin nanoformulations or curcumin nanomedicine) is able to leverage therapeutic benefits by improving bioavailability and pharmacokinetics which in turn improves binding, internalization and targeting of tumor(s). Outcomes using these novel drug delivery systems have been discussed in detail. This review also describes the tumor-specific drug delivery system(s) that can be highly effective in destroying tumors. Such new approaches are expected to lead to clinical trials and to improve cancer therapeutics.
Nanotechnology; curcumin nanomedicine; drug delivery; cancer therapy; chemo-prevention; and tumor targeting
Fungi are the second most abundant type of human pathogens. Invasive fungal pathogens are leading causes of life-threatening infections in clinical settings. Toxicity to the host and drug-resistance are two major deleterious issues associated with existing antifungal agents. Increasing a host’s tolerance and/or immunity to fungal pathogens has potential to alleviate these problems. A host’s tolerance may be improved by modulating the immune system such that it responds more rapidly and robustly in all facets, ranging from the recognition of pathogens to their clearance from the host. An understanding of biological processes and genes that are perturbed during attempted fungal exposure, colonization, and/or invasion will help guide the identification of endogenous immunomodulators and/or small molecules that activate host-immune responses such as specialized adjuvants.
In this study, we present computational techniques and approaches using publicly available transcriptional data sets, to predict immunomodulators that may act against multiple fungal pathogens. Our study analyzed data sets derived from host cells exposed to five fungal pathogens, namely, Alternaria alternata, Aspergillus fumigatus, Candida albicans, Pneumocystis jirovecii, and Stachybotrys chartarum. We observed statistically significant associations between host responses to A. fumigatus and C. albicans. Our analysis identified biological processes that were consistently perturbed by these two pathogens. These processes contained both immune response-inducing genes such as MALT1, SERPINE1, ICAM1, and IL8, and immune response-repressing genes such as DUSP8, DUSP6, and SPRED2. We hypothesize that these genes belong to a pool of common immunomodulators that can potentially be activated or suppressed (agonized or antagonized) in order to render the host more tolerant to infections caused by A. fumigatus and C. albicans.
Our computational approaches and methodologies described here can now be applied to newly generated or expanded data sets for further elucidation of additional drug targets. Moreover, identified immunomodulators may be used to generate experimentally testable hypotheses that could help in the discovery of broad-spectrum immunotherapeutic interventions. All of our results are available at the following supplementary website: http://bioinformatics.cs.vt.edu/~murali/supplements/2013-kidane-bmc
Host-oriented therapy; Broad-spectrum target; Immunomodulation; Drug-resistance; Drug-target discovery; Immunotherapy
Over the past 2 decades, major advances in our understanding of pulmonary arterial hypertension (PAH) have led to the development of new targeted therapeutics and management strategies that have provided benefits to patients with this devastating disease. Despite such improvements, no therapies are curative, and PAH remains a progressive disease associated with high morbidity and suboptimal survival in many patients. Clinical research in PAH is currently at a crossroads. To move forward, not only are new therapies needed, but novel approaches to clinical trial design are also required. Trials should be designed to assess the longer-term benefits of investigational therapies in what has become a chronic disease. Moreover, there is a need to consider moving away from short-term trials that use markers such as the 6-minute walk distance as a measure of exercise capacity as primary end points to longer-term, event-driven trials with composite end points made up of clinically relevant measures that better reflect the ultimate goals of reducing morbidity and mortality. A shift in trial design may also be useful in overcoming some of the muted results from recent pivotal phase III studies of combination therapy by allowing the potential of these regimens to be more comprehensively assessed.
pulmonary arterial hypertension; mortality; morbidity; surrogate markers; clinical worsening, macitentan.
Microarray experiments can simultaneously identify thousands of genes that show significant perturbation in expression between two experimental conditions. Response networks, computed through the integration of gene interaction networks with expression perturbation data, may themselves contain tens of thousands of interactions. Gene set enrichment has become standard for summarizing the results of these analyses in terms functionally coherent collections of genes such as biological processes. However, even these methods can yield hundreds of enriched functions that may overlap considerably.
We describe a new technique called Markov chain Monte Carlo Biological Process Networks (MCMC-BPN) capable of reporting a highly non-redundant set of links between processes that describe the molecular interactions that are perturbed under a specific biological context. Each link in the BPN represents the perturbed interactions that serve as the interfaces between the two processes connected by the link.
We apply MCMC-BPN to publicly available liver-related datasets to demonstrate that the networks formed by the most probable inter-process links reported by MCMC-BPN show high relevance to each biological condition. We show that MCMC-BPN’s ability to discern the few key links from in a very large solution space by comparing results from two other methods for detecting inter-process links.
MCMC-BPN is successful in using few inter-process links to explain as many of the perturbed gene-gene interactions as possible. Thereby, BPNs summarize the important biological trends within a response network by reporting a digestible number of inter-process links that can be explored in greater detail.
Molecular interaction networks; Gene expression data; Networks of biological processes; Data integration; Markov chain Monte Carlo
Pressure overload due to aortic stenosis (AS) causes maladaptive ventricular and vascular remodeling that can lead to pulmonary hypertension, heart failure symptoms, and adverse outcomes. Retarding or reversing this maladaptive remodeling and its unfavorable hemodynamic consequences has potential to improve morbidity and mortality. Preclinical models of pressure overload have shown that phosphodiesterase type 5 (PDE5) inhibition is beneficial, however the use of PDE5 inhibitors in patients with AS is controversial because of concerns about vasodilation and hypotension.
Methods and Results
We evaluated the safety and hemodynamic response of 20 subjects with severe symptomatic AS (mean aortic valve area 0.7±0.2 cm2, ejection fraction 60±14%) who received a single oral dose of sildenafil (40mg or 80mg). Compared to baseline, after 60 minutes sildenafil reduced systemic (−12%, p<0.001) and pulmonary (−29%, p=0.002) vascular resistance, mean pulmonary artery (−25%, p<0.001) and wedge (−17%, p<0.001) pressure, and increased systemic (+13%, p<0.001) and pulmonary (+45%, p<0.001) vascular compliance and stroke volume index (+8%, p=0.01). These changes were not dose dependent. Sildenafil caused a modest decrease in mean systemic arterial pressure (−11%, p<0.001), but was well-tolerated with no episodes of symptomatic hypotension.
This study shows for the first time that a single dose of a PDE5 inhibitor is safe and well-tolerated in patients with severe AS and is associated with acute improvements in pulmonary and systemic hemodynamics resulting in biventricular unloading. These findings support the need for longer-term studies to evaluate the role of PDE5 inhibition as adjunctive medical therapy in patients with AS.
aortic valve stenosis; heart failure; phosphodiesterase type 5 inhibitors; pulmonary hypertension; hemodynamics
The emergence of drug-resistant pathogen strains and new infectious agents pose major challenges to public health. A promising approach to combat these problems is to target the host’s genes or proteins, especially to discover targets that are effective against multiple pathogens, i.e., host-oriented broad-spectrum (HOBS) drug targets. An important first step in the discovery of such drug targets is the identification of host responses that are commonly perturbed by multiple pathogens.
In this paper, we present a methodology to identify common host responses elicited by multiple pathogens. First, we identified host responses perturbed by each pathogen using a gene set enrichment analysis of publicly available genome-wide transcriptional datasets. Then, we used biclustering to identify groups of host pathways and biological processes that were perturbed only by a subset of the analyzed pathogens. Finally, we tested the enrichment of each bicluster in human genes that are known drug targets, on the basis of which we elicited putative HOBS targets for specific groups of bacterial pathogens. We identified 84 up-regulated and three down-regulated statistically significant biclusters. Each bicluster contained a group of pathogens that commonly dysregulated a group of biological processes. We validated our approach by checking whether these biclusters correspond to known hallmarks of bacterial infection. Indeed, these biclusters contained biological process such as inflammation, activation of dendritic cells, pro- and anti- apoptotic responses and other innate immune responses. Next, we identified biclusters containing pathogens that infected the same tissue. After a literature-based analysis of the drug targets contained in these biclusters, we suggested new uses of the drugs Anakinra, Etanercept, and Infliximab for gastrointestinal pathogens Yersinia enterocolitica, Helicobacter pylori kx2 strain, and enterohemorrhagic Escherichia coli and the drug Simvastatin for hematopoietic pathogen Ehrlichia chaffeensis.
Using a combination of automated analysis of host-response gene expression data and manual study of the literature, we have been able to suggest host-oriented treatments for specific bacterial infections. The analyses and suggestions made in this study may be utilized to generate concrete hypothesis on which gene sets to probe further in the quest for HOBS drug targets for bacterial infections. All our results are available at the following supplementary website: http://bioinformatics.cs.vt.edu/ murali/supplements/2013-kidane-plos-one
Curcumin, a natural diphenolic compound derived from turmeric Curcuma longa, has proven to be a modulator of intracellular signaling pathways that control cancer cell growth, inflammation, invasion, apoptosis and cell death, revealing its anticancer potential. In this review, we focus on the design and development of nanoparticles, self-assemblies, nanogels, liposomes and complex fabrication for sustained and efficient curcumin delivery. We also discuss the anticancer applications and clinical benefits of nanocurcumin formulations. Only a few novel multifunctional and composite nanosystem strategies offer simultaneous therapy as well as imaging characteristics. We also summarize the challenges to developing curcumin delivery platforms and up-to-date solutions for improving curcumin bioavailability and anticancer potential for therapy.
Infectious diseases result in millions of deaths each year. Physical interactions between pathogen and host proteins often form the basis of such infections. While a number of methods have been proposed for predicting protein–protein interactions (PPIs), they have primarily focused on intra-species protein–protein interactions.
We present an application of a supervised learning method for predicting physical interactions between host and pathogen proteins, using the human–HIV system. Using a Support Vector Machine with a linear kernel, we explore the use of a number of features including domain profiles, protein sequence k-mers, and properties of human proteins in a human PPI network. We achieve the best cross-validation performance when we use a combination of all three of these features. At a precision value of 70% we obtain recall values greater than 40%, depending on the ratio of positive examples to negative examples used during training. We use a classifier trained using these features to predict new PPIs between human and HIV proteins. We focus our discussion on those predicted interactions that involve human proteins known to be critical for HIV replication and propagation. Examples of predicted interactions with support in the literature include those necessary for viral attachment to the host membrane and subsequent invasion of the host cell.
Unlike intra-species PPIs, host–pathogen PPIs have not yet been experimentally detected on a large scale, though they are likely to play important roles in pathogenesis and disease outcomes. Computational methods that can robustly and accurately predict host–pathogen PPIs hold the promise of guiding future experiments and gaining insights into potential mechanisms of pathogenesis.
Host–pathogen interactions; Protein interaction prediction; Systems biology; Infectious disease
The next generation magnetic nanoparticles (MNPs) with theranostic applications have attracted significant attention and will greatly improve nanomedicine in cancer therapeutics. Such novel MNP formulations must have ultra-low particle size, high inherent magnetic properties, effective imaging, drug targeting, and drug delivery properties. To achieve these characteristic properties, a curcumin-loaded MNP (MNP-CUR) formulation was developed.
MNPs were prepared by chemical precipitation method and loaded with curcumin (CUR) using diffusion method. The physicochemical properties of MNP-CUR were characterized using dynamic light scattering, transmission electron microscopy, and spectroscopy. The internalization of MNP-CUR was achieved after 6 hours incubation with MDA-MB-231 breast cancer cells. The anticancer potential was evaluated by a tetrazolium-based dye and colony formation assays. Further, to prove MNP-CUR results in superior therapeutic effects over CUR, the mitochondrial membrane potential integrity and reactive oxygen species generation were determined. Magnetic resonance imaging capability and magnetic targeting property were also evaluated.
MNP-CUR exhibited individual particle grain size of ~9 nm and hydrodynamic average aggregative particle size of ~123 nm. Internalized MNP-CUR showed a preferential uptake in MDA-MB-231 cells in a concentration-dependent manner and demonstrated accumulation throughout the cell, which indicates that particles are not attached on the cell surface but internalized through endocytosis. MNP-CUR displayed strong anticancer properties compared to free CUR. MNP-CUR also amplified loss of potential integrity and generation of reactive oxygen species upon treatment compared to free CUR. Furthermore, MNP-CUR exhibited superior magnetic resonance imaging characteristics and significantly increased the targeting capability of CUR.
MNP-CUR exhibits potent anticancer activity along with imaging and magnetic targeting capabilities. This approach can be extended to preclinical and clinical use and may have importance in cancer treatment and cancer imaging in the future. Further, if these nanoparticles can functionalize with antibody/ligands, they will serve as novel platforms for multiple biomedical applications.
magnetic nanoparticles; drug delivery systems; magnetic resonance imaging; nanomedicine; cancer therapeutics; biomedical applications
We have developed a multi-layer approach for the synthesis of water-dispersible superparamagnetic iron oxide nanoparticles for hyperthermia, magnetic resonance imaging (MRI) and drug delivery applications. In this approach, iron oxide core nanoparticles were obtained by precipitation of iron salts in the presence of ammonia and provided β-cyclodextrin and pluronic polymer (F127) coatings. This formulation (F127250) was highly water dispersible which allowed encapsulation of the anti-cancer drug(s) in β-cyclodextrin and pluronic polymer for sustained drug release. The F127250 formulation has exhibited superior hyperthermia effects over time under alternating magnetic field compared to pure magnetic nanoparticles (MNP) and β-cyclodextrin coated nanoparticles (CD200). Additionally, the improved MRI characteristics were also observed for the F127250 formulation in agar gel and in cisplatin resistant ovarian cancer cells (A12780CP) compared to MNP and CD200 formulations. Furthermore, the drug loaded formulation of F127250 exhibited many folds of imaging contrast properties. Due to the internalization capacity of the F127250 formulation, its curcumin loaded formulation (F127250-CUR) exhibited almost equivalent inhibition effects on A2780CP (ovarian), MDA-MB-231 (breast), and PC3 (prostate) cancer cells even though curcumin release was only 40%. The improved therapeutic effects were verified by examining molecular effects using Western blotting and transmission electron microscopic (TEM) studies. F127250-CUR also exhibited haemocompatibility, suggesting a nanochemo-therapuetic agent for cancer therapy.
Magnetic nanoparticles; multi-layer coating; MRI; drug delivery; hyperthermia
Dry eye syndrome is a multifactorial chronic disabling disease mainly caused by the functional disruptions in the lacrimal gland. The treatment involves palliation like ocular surface lubrication and rehydration. Cell therapy involving replacement of the gland is a promising alternative for providing long-term relief to patients. This study aimed to establish functionally competent lacrimal gland cultures in–vitro and explore the presence of stem cells in the native gland and the established in-vitro cultures.
Fresh human lacrimal gland from patients undergoing exenteration was harvested for cultures after IRB approval. The freshly isolated cells were evaluated by flow cytometry for expression of stem cell markers ABCG2, high ALDH1 levels and c-kit. Cultures were established on Matrigel, collagen and HAM and the cultured cells evaluated for the presence of stem cell markers and differentiating markers of epithelial (E-cadherin, EpCAM), mesenchymal (Vimentin, CD90) and myofibroblastic (α-SMA, S-100) origin by flow cytometry and immunocytochemistry. The conditioned media was tested for secretory proteins (scIgA, lactoferrin, lysozyme) post carbachol (100 µM) stimulation by ELISA.
Native human lacrimal gland expressed ABCG2 (mean±SEM: 3.1±0.61%), high ALDH1 (3.8±1.26%) and c-kit (6.7±2.0%). Lacrimal gland cultures formed a monolayer, in order of preference on Matrigel, collagen and HAM within 15–20 days, containing a heterogeneous population of stem-like and differentiated cells. The epithelial cells formed ‘spherules’ with duct like connections, suggestive of ductal origin. The levels of scIgA (47.43 to 61.56 ng/ml), lysozyme (24.36 to 144.74 ng/ml) and lactoferrin (32.45 to 40.31 ng/ml) in the conditioned media were significantly higher than the negative controls (p<0.05 for all comparisons).
The study reports the novel finding of establishing functionally competent human lacrimal gland cultures in-vitro. It also provides preliminary data on the presence of stem cells and duct-like cells in the fresh and in-vitro cultured human lacrimal gland. These significant findings could pave way for cell therapy in future.
Two commonly used culture systems in hepatic tissue engineering are the collagen sandwich (CS) and monolayers of cells. In this study, genome-wide gene expression profiles of primary hepatocytes were measured over an 8-day period for each cell culture system using Affymetrix GeneChips and compared via gene set enrichment analysis to elicit biologically meaningful information at the level of gene sets. Our results demonstrate that gene expression in hepatocytes in CS cultures steadily and comprehensively diverges from that in monolayer cultures. Gene sets up-regulated in CS cultures include several associated with liver metabolic and synthesis functions, such as metabolism of lipids, amino acids, carbohydrates, and alcohol, and synthesis of bile acids. Monooxygenases such as Cytochrome-P450 enzymes do not show any change between the culture systems after 1 day, but exhibit significant up-regulation in CS cultures after 3 days in comparison to hepatocyte monolayers. These data provide insights into the up- and down-regulation of several liver-critical gene sets and their subsequent effects on liver-specific functions. These results provide a baseline for further explorations into the systems biology of engineered liver mimics.
Group II pulmonary hypertension commonly occurs in the setting of a pressure overloaded left ventricle that is also conducive to the development heart failure with preserved ejection fraction. Population trends and a high prevalence of underlying causative conditions, such as essential hypertension or aortic stenosis, have increased awareness of the pressure overloaded left ventricle and associated Group II pulmonary hypertension. Patients often exhibit poor exercise tolerance and signs of heart failure indistinguishable from systolic heart failure; but effective medical treatments in this area have been lacking. Recent pre-clinical work has shed light on how the down-regulated nitric oxide – cyclic GMP pathway (within the myocardium and pulmonary vasculature) contributes to the pathophysiology of these associated conditions. This article will discuss the impact of the nitric oxide – cyclic GMP pathway on the pathogenesis of the pressure overloaded left ventricle and Group II pulmonary hypertension, and will also introduce the potential therapeutic value of modulating this pathway.
HIV Dependency Factors (HDFs) are a class of human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three previous genome-wide RNAi experiments identified HDF sets with little overlap. We combine data from these three studies with a human protein interaction network to predict new HDFs, using an intuitive algorithm called SinkSource and four other algorithms published in the literature. Our algorithm achieves high precision and recall upon cross validation, as do the other methods. A number of HDFs that we predict are known to interact with HIV proteins. They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV. We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. More generally, if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon, our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other.
Medicines to cure infectious diseases usually target proteins in the pathogens. Since pathogens have short life cycles, the targeted proteins can rapidly evolve and make the medicines ineffective, especially in viruses such as HIV. However, since viruses have very small genomes, they must exploit the cellular machinery of the host to propagate. Therefore, disrupting the activity of selected host proteins may impede viruses. Three recent experiments have discovered hundreds of such proteins in human cells that HIV depends upon. Surprisingly, these three sets have very little overlap. In this work, we demonstrate that this discrepancy can be explained by considering physical interactions between the human proteins in these studies. Moreover, we exploit these interactions to predict new dependency factors for HIV. Our predictions show very significant overlaps with human proteins that are known to interact with HIV proteins and with human cellular processes that are known to be subverted by the virus. Most importantly, we show that proteins predicted by us may play a prominent role in affecting HIV-related disease progression in lymph nodes. Therefore, our predictions constitute a powerful resource for experimentalists who desire to discover new human proteins that can control the spread of HIV.
Previous studies have suggested that azithromycin improves lung function in lung transplant recipients with bronchiolitis obliterans syndrome (BOS). However, these studies did not include a non-treated BOS control cohort or perform survival analysis. This study was undertaken to estimate the effect of azithromycin treatment on survival in lung transplant recipients with BOS.
We conducted a retrospective cohort study of consecutive lung transplant recipients who developed BOS between 1999 and 2007. An association between azithromycin treatment and death was assessed using univariate and multivariable time-dependent Cox regression analysis.
Of the 178 recipients that developed BOS in our study, 78 developed BOS after 2003 and were treated with azithromycin. The azithromycin treated and untreated cohorts had similar baseline characteristics. Univariate analysis demonstrated that azithromycin treatment was associated with a survival advantage and this beneficial treatment effect was more pronounced when treatment was initiated during BOS stage 1. Multivariable analysis demonstrated azithromycin treatment during BOS stage 1 (adjusted hazard ratio=0.23, p=0.01) and absolute FEV1 value at the time of BOS stage 1 (adjusted hazard ratio=0.52, p=0.003) were both associated with a decreased risk for death.
In lung transplant recipients with BOS stage 1, azithromycin treatment initiated before BOS stage 2 was independently associated with a significant reduction in the risk of death. This finding supports the need for a randomized controlled trial to confirm the impact of azithromycin on survival in lung transplant recipients.
The liver plays a vital role in glucose homeostasis, the synthesis of bile acids and the detoxification of foreign substances. Liver culture systems are widely used to test adverse effects of drugs and environmental toxicants. The two most prevalent liver culture systems are hepatocyte monolayers (HMs) and collagen sandwiches (CS). Despite their wide use, comprehensive transcriptional programs and interaction networks in these culture systems have not been systematically investigated. We integrated an existing temporal transcriptional dataset for HM and CS cultures of rat hepatocytes with a functional interaction network of rat genes. We aimed to exploit the functional interactions to identify statistically significant linkages between perturbed biological processes. To this end, we developed a novel approach to compute Contextual Biological Process Linkage Networks (CBPLNs). CBPLNs revealed numerous meaningful connections between different biological processes and gene sets, which we were successful in interpreting within the context of liver metabolism. Multiple phenomena captured by CBPLNs at the process level such as regulation, downstream effects, and feedback loops have well described counterparts at the gene and protein level. CBPLNs reveal high-level linkages between pathways and processes, making the identification of important biological trends more tractable than through interactions between individual genes and molecules alone. Our approach may provide a new route to explore, analyze, and understand cellular responses to internal and external cues within the context of the intricate networks of molecular interactions that control cellular behavior.