Effectiveness and Preclinical Safety Profile of Doxycycline to Be Used “Off-Label” to Induce Therapeutic Transgene Expression in a Phase I Clinical Trial for Glioma

Glioblastoma multiforme (GBM) is the most common malignant primary brain cancer in adults; it carries a dismal prognosis despite improvements in standard of care. We developed a combined gene therapy strategy using (1) herpes simplex type 1-thymidine kinase in conjunction with the cytotoxic prodrug ganciclovir to kill actively proliferating tumor cells and (2) doxycycline (DOX)-inducible Fms-like tyrosine kinase 3 ligand (Flt3L), an immune stimulatory molecule that induces anti-GBM immunity. As a prelude to a phase I clinical trial, we examined the efficacy and safety of this approach (Muhammad et al., 2010, 2012). In the present article, we investigated the efficacy and safety of the ?off-label? use of the antibiotic DOX to turn on the high-capacity adenoviral vector (HC-Ad) encoding therapeutic Flt3L expression. DOX-inducible Flt3L expression in male Lewis rats was assessed using DOX doses of 30.8?mg/kg/day (low-DOX) or 46.2?mg/kg/day (high-DOX), which are allometrically equivalent (Voisin et al., 1990) to the human doses that are recommended for the treatment of infections: 200 or 300?mg/day. Naïve rats were intracranially injected with 1?109 viral particles of HC-Ad-TetOn-Flt3L, and expression of the therapeutic transgene, that is, Flt3L, was assessed using immunohistochemistry in brain sections after 2 weeks of DOX administration via oral gavage. The results show robust expression of Flt3L in the rat brain parenchyma in areas near the injection site in both the low-DOX and the high-DOX groups, suggesting that Flt3L will be expressed in human glioma patients at a DOX dose of 200 or 300?mg/day. These doses have been approved by the U.S. Food and Drug Administration to treat infections in humans and would thus be considered safe for an off-label use to treat GBM patients undergoing HC-Ad-mediated gene therapy in a phase I clinical trial.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140104/1/humc.2013.139.pd

Use of chromatin immunoprecipitation (ChIP) to detect transcription factor binding to highly homologous promoters in chromatin isolated from unstimulated and activated primary human B cells

The Chromatin Immunoprecipiation (ChIP) provides a powerful technique for identifying the in vivo association of transcription factors with regulatory elements. However, obtaining meaningful information for promoter interactions is extremely challenging when the promoter is a member of a class of highly homologous elements. Use of PCR primers with small numbers of mutations can limit cross-hybridization with non-targeted sequences and distinguish a pattern of binding for factors with the regulatory element of interest. In this report, we demonstrate the selective in vivo association of NF-κB, p300 and CREB with the human Iγ1 promoter located in the intronic region upstream of the Cγ1 exons in the immunoglobulin heavy chain locus. These methods have the ability to extend ChIP analysis to promoters with a high degree of homology

Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

We describe AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13 Submissions were made by three free-modelling methods which combine the predictions of three neural networks. All three systems were guided by predictions of distances between pairs of residues produced by a neural network. Two systems assembled fragments produced by a generative neural network, one using scores from a network trained to regress GDT_TS. The third system shows that simple gradient descent on a properly constructed potential is able to perform on-par with more expensive traditional search techniques and without requiring domain segmentation. In the CASP13 free-modelling assessors' ranking by summed z-scores, this system scored highest with 68.3 vs 48.2 for the next closest group. (An average GDT_TS of 61.4.) The system produced high-accuracy structures (with GDT_TS scores of 70 or higher) for 11 out of 43 free-modelling domains. Despite not explicitly using template information, the results in the template category were comparable to the best performing template-based methods

Improved protein structure prediction using potentials from deep learning

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)—a blind assessment of the state of the field—AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined7

Step-by-step design of proteins for small molecule interaction: a review on recent milestones

Protein design is the field of synthetic biology that aims at developing de-novo custom made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the art software is briefly explored.publishe

Quadrupole Anisotropy in Dihadron Azimuthal Correlations in Central dd++Au Collisions at sNN\sqrt{s_{_{NN}}}=200 GeV

The PHENIX collaboration at the Relativistic Heavy Ion Collider (RHIC) reports measurements of azimuthal dihadron correlations near midrapidity in $d$$+$Au collisions at $\sqrt{s_{_{NN}}}$=200 GeV. These measurements complement recent analyses by experiments at the Large Hadron Collider (LHC) involving central $p$$+$Pb collisions at $\sqrt{s_{_{NN}}}$=5.02 TeV, which have indicated strong anisotropic long-range correlations in angular distributions of hadron pairs. The origin of these anisotropies is currently unknown. Various competing explanations include parton saturation and hydrodynamic flow. We observe qualitatively similar, but larger, anisotropies in $d$$+$Au collisions compared to those seen in $p$$+$Pb collisions at the LHC. The larger extracted $v_2$ values in $d$$+$Au collisions at RHIC are consistent with expectations from hydrodynamic calculations owing to the larger expected initial-state eccentricity compared with that from $p$$+$Pb collisions. When both are divided by an estimate of the initial-state eccentricity the scaled anisotropies follow a common trend with multiplicity that may extend to heavy ion data at RHIC and the LHC, where the anisotropies are widely thought to arise from hydrodynamic flow.Comment: 375 authors, 7 pages, 5 figures. Published in Phys. Rev. Lett. v2 has minor changes to text and figures in response to PRL referee suggestions. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.htm

Cross section for bbˉb\bar{b} production via dielectrons in d++Au collisions at sNN=200\sqrt{s_{_{NN}}}=200 GeV

We report a measurement of $e^+e^-$ pairs from semileptonic heavy-flavor decays in $d$$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV. Exploring the mass and transverse-momentum dependence of the yield, the bottom decay contribution can be isolated from charm, and quantified by comparison to {\sc pythia} and {\sc mc@nlo} simulations. The resulting $b\bar{b}$-production cross section is $\sigma^{d{\rm Au}}_{b\bar{b}}=1.37{\pm}0.28({\rm stat}){\pm}0.46({\rm syst})$~mb, which is equivalent to a nucleon-nucleon cross section of $\sigma^{NN}_{bb}=3.4\pm0.8({\rm stat}){\pm}1.1({\rm syst})\ \mu$b.Comment: 375 authors, 16 pages, 8 figures, 7 tables, 2008 data. Submitted to Phys. Rev. C Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.htm

Suppression of back-to-back hadron pairs at forward rapidity in d+Au Collisions at sqrt(s_NN)=200 GeV

Back-to-back hadron pair yields in d+Au and p+p collisions at sqrt(s_NN)=200 GeV were measured with the PHENIX detector at the Relativistic Heavy Ion Collider. Rapidity separated hadron pairs were detected with the trigger hadron at pseudorapidity |eta|<0.35 and the associated hadron at forward rapidity (deuteron direction, 3.0<eta<3.8). Pairs were also detected with both hadrons measured at forward rapidity; in this case the yield of back-to-back hadron pairs in d+Au collisions with small impact parameters is observed to be suppressed by a factor of 10 relative to p+p collisions. The kinematics of these pairs is expected to probe partons in the Au nucleus with low fraction x of the nucleon momenta, where the gluon densities rise sharply. The observed suppression as a function of nuclear thickness, p_T, and eta points to cold nuclear matter effects arising at high parton densities.Comment: 381 authors, 6 pages, 4 figures. Published in Phys. Rev. Lett. (http://link.aps.org/doi/10.1103/PhysRevLett.107.172301). v3 has minor changes to match published version (http://www.phenix.bnl.gov/phenix/WWW/info/pp1/128/PhysRevLett.107.172301) Plain text data tables for points plotted in figures are publicly available at http://www.phenix.bnl.gov/phenix/WWW/info/data/ppg128_data.htm

Centrality categorization for R_{p(d)+A} in high-energy collisions

High-energy proton- and deuteron-nucleus collisions provide an excellent tool for studying a wide array of physics effects, including modifications of parton distribution functions in nuclei, gluon saturation, and color neutralization and hadronization in a nuclear environment, among others. All of these effects are expected to have a significant dependence on the size of the nuclear target and the impact parameter of the collision, also known as the collision centrality. In this article, we detail a method for determining centrality classes in p(d)+A collisions via cuts on the multiplicity at backward rapidity (i.e., the nucleus-going direction) and for determining systematic uncertainties in this procedure. For d+Au collisions at sqrt(s_NN) = 200 GeV we find that the connection to geometry is confirmed by measuring the fraction of events in which a neutron from the deuteron does not interact with the nucleus. As an application, we consider the nuclear modification factors R_{p(d)+A}, for which there is a potential bias in the measured centrality dependent yields due to auto-correlations between the process of interest and the backward rapidity multiplicity. We determine the bias correction factor within this framework. This method is further tested using the HIJING Monte Carlo generator. We find that for d+Au collisions at sqrt(s_NN)=200 GeV, these bias corrections are small and vary by less than 5% (10%) up to p_T = 10 (20) GeV. In contrast, for p+Pb collisions at sqrt(s_NN) = 5.02 TeV we find these bias factors are an order of magnitude larger and strongly p_T dependent, likely due to the larger effect of multi-parton interactions.Comment: 375 authors, 18 pages, 16 figures, 4 tables. Submitted to Phys. Rev. C. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.htm

Transverse-Momentum Dependence of the J/psi Nuclear Modification in d+Au Collisions at sqrt(s_NN)=200 GeV

We present measured J/psi production rates in d+Au collisions at sqrt(s_NN) = 200 GeV over a broad range of transverse momentum (p_T=0-14 GeV/c) and rapidity (-2.2<y<2.2). We construct the nuclear-modification factor R_dAu for these kinematics and as a function of collision centrality (related to impact parameter for the R_dAu collision). We find that the modification is largest for collisions with small impact parameters, and observe a suppression (R_dAu<1) for p_T<4 GeV/c at positive rapidities. At negative rapidity we observe a suppression for p_T1) for p_T>2 GeV/c. The observed enhancement at negative rapidity has implications for the observed modification in heavy-ion collisions at high p_T.Comment: 384 authors, 24 pages, 19 figures, 13 tables. Submitted to Phys. Rev. C. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are publicly available at http://www.phenix.bnl.gov/phenix/WWW/info/data/ppg123_data.htm