The use of thermographic imaging to evaluate therapeutic response in human tumour xenograft models

YesNon-invasive methods to monitor tumour growth are an important goal in cancer drug development. Thermographic imaging systems offer potential in this area, since a change in temperature is known to be induced due to changes within the tumour microenvironment. This study demonstrates that this imaging modality can be applied to a broad range of tumour xenografts and also, for the first time, the methodology’s suitability to assess anti-cancer agent efficacy. Mice bearing subcutaneously implanted H460 lung cancer xenografts were treated with a novel vascular disrupting agent, ICT-2552, and the cytotoxin doxorubicin. The effects on tumour temperature were assessed using thermographic imaging over the first 6 hours post-administration and subsequently a further 7 days. For ICT-2552 a significant initial temperature drop was observed, whilst for both agents a significant temperature drop was seen compared to controls over the longer time period. Thus thermographic imaging can detect functional differences (manifesting as temperature reductions) in the tumour response to these anti-cancer agents compared to controls. Importantly, these effects can be detected in the first few hours following treatment and therefore the tumour is observable non-invasively. As discussed, this technique will have considerable 3Rs benefits in terms of reduction and refinement of animal use.University of Bradfor

The evolution and storage of primitive melts in the Eastern Volcanic Zone of Iceland: the 10 ka Grímsvötn tephra series (i.e. the Saksunarvatn ash)

Major, trace and volatile elements were measured in a suite of primitive macrocrysts and melt inclusions from the thickest layer of the 10 ka Grímsvötn tephra series (i.e. Saksunarvatn ash) at Lake Hvítárvatn in central Iceland. In the absence of primitive tholeiitic eruptions (MgO > 7 wt.%) within the Eastern Volcanic Zone (EVZ) of Iceland, these crystal and inclusion compositions provide an important insight into magmatic processes in this volcanically productive region. Matrix glass compositions show strong similarities with glass compositions from the AD 1783–84 Laki eruption, confirming the affinity of the tephra series with the Grímsvötn volcanic system. Macrocrysts can be divided into a primitive assemblage of zoned macrocryst cores (An_78–An_92, Mg#_cpx = 82–87, Fo_79.5–Fo_87) and an evolved assemblage consisting of unzoned macrocrysts and the rims of zoned macrocrysts (An_60–An_68, Mg#_cpx = 71–78, Fo_70–Fo_76). Although the evolved assemblage is close to being in equilibrium with the matrix glass, trace element disequilibrium between primitive and evolved assemblages indicates that they were derived from different distributions of mantle melt compositions. Juxtaposition of disequilibrium assemblages probably occurred during disaggregation of incompatible trace element-depleted mushes (mean La/Yb_melt = 2.1) into aphyric and incompatible trace element-enriched liquids (La/Yb_melt = 3.6) shortly before the growth of the evolved macrocryst assemblage. Post-entrapment modification of plagioclase-hosted melt inclusions has been minimal and high-Mg# inclusions record differentiation and mixing of compositionally variable mantle melts that are amongst the most primitive liquids known from the EVZ. Coupled high field strength element (HFSE) depletion and incompatible trace element enrichment in a subset of primitive plagioclase-hosted melt inclusions can be accounted for by inclusion formation following plagioclase dissolution driven by interaction with plagioclase-undersaturated melts. Thermobarometric calculations indicate that final crystal-melt equilibration within the evolved assemblage occurred at ~1140°C and 0.0–1.5 kbar. Considering the large volume of the erupted tephra and textural evidence for rapid crystallisation of the evolved assemblage, 0.0–1.5 kbar is considered unlikely to represent a pressure of long-term magma accumulation and storage. Multiple thermometers indicate that the primitive assemblage crystallised at high temperatures of 1240–1300°C. Different barometers, however, return markedly different crystallisation depth estimates. Raw clinopyroxene-melt pressures of 5.5–7.5 kbar conflict with apparent melt inclusion entrapment pressures of 1.4 kbar. After applying a correction derived from published experimental data, clinopyroxene-melt equilibria return mid-crustal pressures of 4±1.5 kbar, which are consistent with pressures estimated from the major element content of primitive melt inclusions. Long-term storage of primitive magmas in the mid-crust implies that low CO_2 concentrations measured in primitive plagioclase-hosted inclusions (262–800 ppm) result from post-entrapment CO_2 loss during transport through the shallow crust. In order to reconstruct basaltic plumbing system geometries from petrological data with greater confidence, mineral-melt equilibrium models require refinement at pressures of magma storage in Iceland. Further basalt phase equilibria experiments are thus needed within the crucial 1–7 kbar range.D.A.N. was supported by a Natural Environment Research Council studentship (NE/1528277/1) at the start of this project. SIMS analyses were supported by Natural Environment Research Council Ion Microprobe Facility award (IMF508/1013).This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s00410-015-1170-

Analysis of computational approaches for motif discovery

Recently, we performed an assessment of 13 popular computational tools for discovery of transcription factor binding sites (M. Tompa, N. Li, et al., "Assessing Computational Tools for the Discovery of Transcription Factor Binding Sites", Nature Biotechnology, Jan. 2005). This paper contains follow-up analysis of the assessment results, and raises and discusses some important issues concerning the state of the art in motif discovery methods: 1. We categorize the objective functions used by existing tools, and design experiments to evaluate whether any of these objective functions is the right one to optimize. 2. We examine various features of the data sets that were used in the assessment, such as sequence length and motif degeneracy, and identify which features make data sets hard for current motif discovery tools. 3. We identify an important feature that has not yet been used by existing tools and propose a new objective function that incorporates this feature

Discriminative motif discovery in DNA and protein sequences using the DEME algorithm

<p>Abstract</p> <p>Background</p> <p>Motif discovery aims to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences. Discriminative motif finding algorithms aim to increase the sensitivity and selectivity of motif discovery by utilizing a second set of sequences, and searching only for patterns that can differentiate the two sets of sequences. Potential applications of discriminative motif discovery include discovering transcription factor binding site motifs in ChIP-chip data and finding protein motifs involved in thermal stability using sets of orthologous proteins from thermophilic and mesophilic organisms.</p> <p>Results</p> <p>We describe DEME, a discriminative motif discovery algorithm for use with protein and DNA sequences. Input to DEME is two sets of sequences; a "positive" set and a "negative" set. DEME represents motifs using a probabilistic model, and uses a novel combination of global and local search to find the motif that optimally discriminates between the two sets of sequences. DEME is unique among discriminative motif finders in that it uses an informative Bayesian prior on protein motif columns, allowing it to incorporate prior knowledge of residue characteristics. We also introduce four, synthetic, discriminative motif discovery problems that are designed for evaluating discriminative motif finders in various biologically motivated contexts. We test DEME using these synthetic problems and on two biological problems: finding yeast transcription factor binding motifs in ChIP-chip data, and finding motifs that discriminate between groups of thermophilic and mesophilic orthologous proteins.</p> <p>Conclusion</p> <p>Using artificial data, we show that DEME is more effective than a non-discriminative approach when there are "decoy" motifs or when a variant of the motif is present in the "negative" sequences. With real data, we show that DEME is as good, but not better than non-discriminative algorithms at discovering yeast transcription factor binding motifs. We also show that DEME can find highly informative thermal-stability protein motifs. Binaries for the stand-alone program DEME is free for academic use and is available at <url>http://bioinformatics.org.au/deme/</url></p

rMotifGen: random motif generator for DNA and protein sequences

<p>Abstract</p> <p>Background</p> <p>Detection of short, subtle conserved motif regions within a set of related DNA or amino acid sequences can lead to discoveries about important regulatory domains such as transcription factor and DNA binding sites as well as conserved protein domains. In order to help assess motif detection algorithms on motifs with varying properties and levels of conservation, we have developed a computational tool, rMotifGen, with the sole purpose of generating a number of random DNA or protein sequences containing short sequence motifs. Each motif consensus can be user-defined, randomly generated, or created from a position-specific scoring matrix (PSSM). Insertions and mutations within these motifs are created according to user-defined parameters and substitution matrices. The resulting sequences can be helpful in mutational simulations and in testing the limits of motif detection algorithms.</p> <p>Results</p> <p>Two implementations of rMotifGen have been created, one providing a graphical user interface (GUI) for random motif construction, and the other serving as a command line interface. The second implementation has the added advantages of platform independence and being able to be called in a batch mode. rMotifGen was used to construct sample sets of sequences containing DNA motifs and amino acid motifs that were then tested against the Gibbs sampler and MEME packages.</p> <p>Conclusion</p> <p>rMotifGen provides an efficient and convenient method for creating random DNA or amino acid sequences with a variable number of motifs, where the instance of each motif can be incorporated using a position-specific scoring matrix (PSSM) or by creating an instance mutated from its corresponding consensus using an evolutionary model based on substitution matrices. rMotifGen is freely available at: <url>http://bioinformatics.louisville.edu/brg/rMotifGen/</url>.</p

Oestradiol enhances tumour regression induced by B7-1/IL-2 adenoviral gene transfer in a murine model of breast cancer

The majority of breast cancers are oestrogen dependent and although current treatment strategies have improved, approximately 50% of the patients will develop metastasis. New treatments that result in long-term systemic immunity are therefore being developed. We have previously shown that adenoviral gene transfer of B7-I/IL-2 to murine breast cancer induces a high rate of complete turnout regression and systemic immunity. Since oestrogens not only affect breast cancer but also have been shown to modulate immune function and secretion of immune-regulatory cytokines, we explored whether administration of oestradiol altered the immune response induced by an adenoviral vector expressing B7-I/IL-2. An oestrogen-dependent murine breast cancer tumour was used in ovariectomised mice, supplemented either oestradiol or placebo. We report the somewhat unexpected finding that intratumoral injection of adenovirus expressing B7-I/IL-2 induces complete turnout regression in 76% of oestradiol-supplemented mice, while only 18% of the tumours regressed in the oestrogen-depleted group. Cured mice in both groups exhibited a similar CTL response against the tumour antigen. However, intratumoral IFN-? levels, 2 days after B7-I/IL-2 injection, were significantly higher in mice treated with oestradiol compared to placebo. This may be one mechanism explaining the higher response rate of tumours in oestradiol-replenished mice.</p

RNAcontext: A New Method for Learning the Sequence and Structure Binding Preferences of RNA-Binding Proteins

Metazoan genomes encode hundreds of RNA-binding proteins (RBPs). These proteins regulate post-transcriptional gene expression and have critical roles in numerous cellular processes including mRNA splicing, export, stability and translation. Despite their ubiquity and importance, the binding preferences for most RBPs are not well characterized. In vitro and in vivo studies, using affinity selection-based approaches, have successfully identified RNA sequence associated with specific RBPs; however, it is difficult to infer RBP sequence and structural preferences without specifically designed motif finding methods. In this study, we introduce a new motif-finding method, RNAcontext, designed to elucidate RBP-specific sequence and structural preferences with greater accuracy than existing approaches. We evaluated RNAcontext on recently published in vitro and in vivo RNA affinity selected data and demonstrate that RNAcontext identifies known binding preferences for several control proteins including HuR, PTB, and Vts1p and predicts new RNA structure preferences for SF2/ASF, RBM4, FUSIP1 and SLM2. The predicted preferences for SF2/ASF are consistent with its recently reported in vivo binding sites. RNAcontext is an accurate and efficient motif finding method ideally suited for using large-scale RNA-binding affinity datasets to determine the relative binding preferences of RBPs for a wide range of RNA sequences and structures

Extracellular Heat Shock Protein (Hsp)70 and Hsp90α Assist in Matrix Metalloproteinase-2 Activation and Breast Cancer Cell Migration and Invasion

Breast cancer is second only to lung cancer in cancer-related deaths in women, and the majority of these deaths are caused by metastases. Obtaining a better understanding of migration and invasion, two early steps in metastasis, is critical for the development of treatments that inhibit breast cancer metastasis. In a functional proteomic screen for proteins required for invasion, extracellular heat shock protein 90 alpha (Hsp90α) was identified and shown to activate matrix metalloproteinase 2 (MMP-2). The mechanism of MMP-2 activation by Hsp90α is unknown. Intracellular Hsp90α commonly functions with a complex of co-chaperones, leading to our hypothesis that Hsp90α functions similarly outside of the cell. In this study, we show that a complex of co-chaperones outside of breast cancer cells assists Hsp90α mediated activation of MMP-2. We demonstrate that the co-chaperones Hsp70, Hop, Hsp40, and p23 are present outside of breast cancer cells and co-immunoprecipitate with Hsp90α in vitro and in breast cancer conditioned media. These co-chaperones also increase the association of Hsp90α and MMP-2 in vitro. This co-chaperone complex enhances Hsp90α-mediated activation of MMP-2 in vitro, while inhibition of Hsp70 in conditioned media reduces this activation and decreases cancer cell migration and invasion. Together, these findings support a model in which MMP-2 activation by an extracellular co-chaperone complex mediated by Hsp90α increases breast cancer cell migration and invasion. Our studies provide insight into a novel pathway for MMP-2 activation and suggest Hsp70 as an additional extracellular target for anti-metastatic drug development

Computational analyses of eukaryotic promoters

Computational analysis of eukaryotic promoters is one of the most difficult problems in computational genomics and is essential for understanding gene expression profiles and reverse-engineering gene regulation network circuits. Here I give a basic introduction of the problem and recent update on both experimental and computational approaches. More details may be found in the extended references. This review is based on a summer lecture given at Max Planck Institute at Berlin in 2005

Adaptive Evolution in Zinc Finger Transcription Factors

The majority of human genes are conserved among mammals, but some gene families have undergone extensive expansion in particular lineages. Here, we present an evolutionary analysis of one such gene family, the poly–zinc-finger (poly-ZF) genes. The human genome encodes approximately 700 members of the poly-ZF family of putative transcriptional repressors, many of which have associated KRAB, SCAN, or BTB domains. Analysis of the gene family across the tree of life indicates that the gene family arose from a small ancestral group of eukaryotic zinc-finger transcription factors through many repeated gene duplications accompanied by functional divergence. The ancestral gene family has probably expanded independently in several lineages, including mammals and some fishes. Investigation of adaptive evolution among recent paralogs using dN/dS analysis indicates that a major component of the selective pressure acting on these genes has been positive selection to change their DNA-binding specificity. These results suggest that the poly-ZF genes are a major source of new transcriptional repression activity in humans and other primates