Nanodrug-Enhanced Radiofrequency Tumor Ablation: Effect of Micellar or Liposomal Carrier on Drug Delivery and Treatment Efficacy

Purpose To determine the effect of different drug-loaded nanocarriers (micelles and liposomes) on delivery and treatment efficacy for radiofrequency ablation (RFA) combined with nanodrugs. Materials/Methods Fischer 344 rats were used (n = 196). First, single subcutaneous R3230 tumors or normal liver underwent RFA followed by immediate administration of IV fluorescent beads (20, 100, and 500 nm), with fluorescent intensity measured at 4–24 hr. Next, to study carrier type on drug efficiency, RFA was combined with micellar (20 nm) or liposomal (100 nm) preparations of doxorubicin (Dox; targeting HIF-1α) or quercetin (Qu; targeting HSP70). Animals received RFA alone, RFA with Lipo-Dox or Mic-Dox (1 mg IV, 15 min post-RFA), and RFA with Lipo-Qu or Mic-Qu given 24 hr pre- or 15 min post-RFA (0.3 mg IV). Tumor coagulation and HIF-1α orHSP70 expression were assessed 24 hr post-RFA. Third, the effect of RFA combined with IV Lipo-Dox, Mic-Dox, Lipo-Qu, or Mic-Qu (15 min post-RFA) compared to RFA alone on tumor growth and animal endpoint survival was evaluated. Finally, drug uptake was compared between RFA/Lipo-Dox and RFA/Mic-Dox at 4–72 hr. Results: Smaller 20 nm beads had greater deposition and deeper tissue penetration in both tumor (100 nm/500 nm) and liver (100 nm) (p<0.05). Mic-Dox and Mic-Qu suppressed periablational HIF-1α or HSP70 rim thickness more than liposomal preparations (p<0.05). RFA/Mic-Dox had greater early (4 hr) intratumoral doxorubicin, but RFA/Lipo-Dox had progressively higher intratumoral doxorubicin at 24–72 hr post-RFA (p<0.04). No difference in tumor growth and survival was seen between RFA/Lipo-Qu and RFA/Mic-Qu. Yet, RFA/Lipo-Dox led to greater animal endpoint survival compared to RFA/Mic-Dox (p<0.03). Conclusion: With RF ablation, smaller particle micelles have superior penetration and more effective local molecular modulation. However, larger long-circulating liposomal carriers can result in greater intratumoral drug accumulation over time and reduced tumor growth. Accordingly, different carriers provide specific advantages, which should be considered when formulating optimal combination therapies

Detailed prediction of protein sub-nuclear localization

Background Sub-nuclear structures or locations are associated with various nuclear processes. Proteins localized in these substructures are important to understand the interior nuclear mechanisms. Despite advances in high-throughput methods, experimental protein annotations remain limited. Predictions of cellular compartments have become very accurate, largely at the expense of leaving out substructures inside the nucleus making a fine-grained analysis impossible. Results Here, we present a new method (LocNuclei) that predicts nuclear substructures from sequence alone. LocNuclei used a string-based Profile Kernel with Support Vector Machines (SVMs). It distinguishes sub-nuclear localization in 13 distinct substructures and distinguishes between nuclear proteins confined to the nucleus and those that are also native to other compartments (traveler proteins). High performance was achieved by implicitly leveraging a large biological knowledge-base in creating predictions by homology-based inference through BLAST. Using this approach, the performance reached AUC = 0.70–0.74 and Q13 = 59–65%. Travelling proteins (nucleus and other) were identified at Q2 = 70–74%. A Gene Ontology (GO) analysis of the enrichment of biological processes revealed that the predicted sub-nuclear compartments matched the expected functionality. Analysis of protein-protein interactions (PPI) show that formation of compartments and functionality of proteins in these compartments highly rely on interactions between proteins. This suggested that the LocNuclei predictions carry important information about function. The source code and data sets are available through GitHub: https://github.com/Rostlab/LocNuclei . Conclusions LocNuclei predicts subnuclear compartments and traveler proteins accurately. These predictions carry important information about functionality and PPIs

MSAViewer:interactive JavaScript visualization of multiple sequence alignments

Summary: The MSAViewer is a quick and easy visualization and analysis JavaScript component for Multiple Sequence Alignment data of any size. Core features include interactive navigation through the alignment, application of popular color schemes, sorting, selecting and filtering. The MSAViewer is ‘web ready’: written entirely in JavaScript, compatible with modern web browsers and does not require any specialized software. The MSAViewer is part of the BioJS collection of components. Availability and Implementation: The MSAViewer is released as open source software under the Boost Software License 1.0. Documentation, source code and the viewer are available at http://msa.biojs.net/. Supplementary information: Supplementary data are available at Bioinformatics online. Contact: [email protected]

DDRprot: a database of DNA damage response-related proteins

The DNA Damage Response (DDR) signalling network is an essential system that protects the genome’s integrity. The DDRprot database presented here is a resource that integrates manually curated information on the human DDR network and its sub-pathways. For each particular DDR protein, we present detailed information about its function. If involved in post-translational modifications (PTMs) with each other, we depict the position of the modified residue/s in the three-dimensional structures, when resolved structures are available for the proteins. All this information is linked to the original publication from where it was obtained. Phylogenetic information is also shown, including time of emergence and conservation across 47 selected species, family trees and sequence alignments of homologues. The DDRprot database can be queried by different criteria: pathways, species, evolutionary age or involvement in (PTM). Sequence searches using hidden Markov models can be also used.E.A.-L. was supported by the European Commission grant [FP7-REGPOT-2012-2013-1; A.A. was partially supported by the Spanish Ministry of Science and Innovation grant [PS09/02111].Peer reviewe

Systemic siRNA Nanoparticle-Based Drugs Combined with Radiofrequency Ablation for Cancer Therapy

Purpose Radiofrequency thermal ablation (RFA) of hepatic and renal tumors can be accompanied by non-desired tumorigenesis in residual, untreated tumor. Here, we studied the use of micelle-encapsulated siRNA to suppress IL-6-mediated local and systemic secondary effects of RFA. Methods: We compared standardized hepatic or renal RFA (laparotomy, 1 cm active tip at 70±2°C for 5 min) and sham procedures without and with administration of 150nm micelle-like nanoparticle (MNP) anti-IL6 siRNA (DOPE-PEI conjugates, single IP dose 15 min post-RFA, C57Bl mouse:3.5 ug/100ml, Fisher 344 rat: 20ug/200ul), RFA/scrambled siRNA, and RFA/empty MNPs. Outcome measures included: local periablational cellular infiltration (α-SMA+ stellate cells), regional hepatocyte proliferation, serum/tissue IL-6 and VEGF levels at 6-72hr, and distant tumor growth, tumor proliferation (Ki-67) and microvascular density (MVD, CD34) in subcutaneous R3230 and MATBIII breast adenocarcinoma models at 7 days. Results: For liver RFA, adjuvant MNP anti-IL6 siRNA reduced RFA-induced increases in tissue IL-6 levels, α-SMA+ stellate cell infiltration, and regional hepatocyte proliferation to baseline (p<0.04, all comparisons). Moreover, adjuvant MNP anti-IL6- siRNA suppressed increased distant tumor growth and Ki-67 observed in R3230 and MATBIII tumors post hepatic RFA (p<0.01). Anti-IL6 siRNA also reduced RFA-induced elevation in VEGF and tumor MVD (p<0.01). Likewise, renal RFA-induced increases in serum IL-6 levels and distant R3230 tumor growth was suppressed with anti-IL6 siRNA (p<0.01). Conclusions: Adjuvant nanoparticle-encapsulated siRNA against IL-6 can be used to modulate local and regional effects of hepatic RFA to block potential unwanted pro-oncogenic effects of hepatic or renal RFA on distant tumor

LocTree3 prediction of localization

The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other state-of-the-art method. Here, we report the availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18 = 80 ± 3% for eukaryotes and a six-state accuracy Q6 = 89 ± 4% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3

Tools and data services registry: a community effort to document bioinformatics resources

Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand. Here we present a community-driven curation effort, supported by ELIXIR—the European infrastructure for biological information—that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners. As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools