Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.

The nucleus of Comet 67P/Churyumov-Gerasimenko. A new shape model and thermophysical analysis

Context. Comet 67P/Churyumov-Gerasimenko is the target of the European Space Agency Rosetta spacecraft rendez-vous mission. Detailed physical characteristation of the comet before arrival is important for mission planning as well as providing a test bed for ground-based observing and data-analysis methods. Aims: To conduct a long-term observational programme to characterize the physical properties of the nucleus of the comet, via ground-based optical photometry, and to combine our new data with all available nucleus data from the literature. Methods: We applied aperture photometry techniques on our imaging data and combined the extracted rotational lightcurves with data from the literature. Optical lightcurve inversion techniques were applied to constrain the spin state of the nucleus and its broad shape. We performed a detailed surface thermal analysis with the shape model and optical photometry by incorporating both into the new Advanced Thermophysical Model (ATPM), along with all available Spitzer 8-24 μm thermal-IR flux measurements from the literature. Results: A convex triangular-facet shape model was determined with axial ratios b/a = 1.239 and c/a = 0.819. These values can vary by as much as 7% in each axis and still result in a statistically significant fit to the observational data. Our best spin state solution has Psid = 12.76137 ± 0.00006 h, and a rotational pole orientated at Ecliptic coordinates λ = 78°(±10°), β = + 58°(±10°). The nucleus phase darkening behaviour was measured and best characterized using the IAU HG system. Best fit parameters are: G = 0.11 ± 0.12 and HR(1,1,0) = 15.31 ± 0.07. Our shape model combined with the ATPM can satisfactorily reconcile all optical and thermal-IR data, with the fit to the Spitzer 24 μm data taken in February 2004 being exceptionally good. We derive a range of mutually-consistent physical parameters for each thermal-IR data set, including effective radius, geometric albedo, surface thermal inertia and roughness fraction. Conclusions: The overall nucleus dimensions are well constrained and strongly imply a broad nucleus shape more akin to comet 9P/Tempel 1, rather than the highly elongated or "bi-lobed" nuclei seen for comets 103P/Hartley 2 or 8P/Tuttle. The derived low thermal inertia of

Convergent genetic and expression data implicate immunity in Alzheimer's disease

Background: Late-onset Alzheimer's disease (AD) is heritable with 20 genes showing genome-wide association in the International Genomics of Alzheimer's Project (IGAP). To identify the biology underlying the disease, we extended these genetic data in a pathway analysis.

Methods: The ALIGATOR and GSEA algorithms were used in the IGAP data to identify associated functional pathways and correlated gene expression networks in human brain.

Results: ALIGATOR identified an excess of curated biological pathways showing enrichment of association. Enriched areas of biology included the immune response (P = 3.27 X 10(-12) after multiple testing correction for pathways), regulation of endocytosis (P = 1.31 X 10(-11)), cholesterol transport (P = 2.96 X 10(-9)), and proteasome-ubiquitin activity (P = 1.34 X 10(-6)). Correlated gene expression analysis identified four significant network modules, all related to the immune response (corrected P = .002-.05).

Conclusions: The immime response, regulation of endocytosis, cholesterol transport, and protein ubiquitination represent prime targets for AD therapeutics. (C) 2015 Published by Elsevier Inc. on behalf of The Alzheimer's Association.

A kinetic analysis methodology to elucidate the roles of metal, support and solvent for the hydrogenation of 4-phenyl-2-butanone over Pt/TiO2

The rate and, more importantly, selectivity (ketone vs aromatic ring) of the hydrogenation of 4-phenyl-2-butanone over a Pt/TiO2 catalyst have been shown to vary with solvent. In this study, a fundamental kinetic model for this multi-phase reaction has been developed incorporating statistical analysis methods to strengthen the foundations of mechanistically sound kinetic models. A 2-site model was determined to be most appropriate, describing aromatic hydrogenation (postulated to be over a platinum site) and ketone hydrogenation (postulated to be at the platinum–titania interface). Solvent choice has little impact on the ketone hydrogenation rate constant but strongly impacts aromatic hydrogenation due to solvent-catalyst interaction. Reaction selectivity is also correlated to a fitted product adsorption constant parameter. The kinetic analysis method shown has demonstrated the role of solvents in influencing reactant adsorption and reaction selectivity.

Comparative Study of Different Methods for the Prediction of Drug− Polymer Solubility

In this study, a comparison of different methods to predict drug−polymer solubility was carried out on binary systems consisting of five model drugs (paracetamol, chloramphenicol, celecoxib, indomethacin, and felodipine) and polyvinylpyrrolidone/vinyl acetate copolymers (PVP/VA) of different monomer weight ratios. The drug−polymer solubility at 25 °C was predicted using the Flory−Huggins model, from data obtained at elevated temperature using thermal analysis methods based on the recrystallization of a supersaturated amorphous solid dispersion and two variations of the melting point depression method. These predictions were compared with the solubility in the low molecular weight liquid analogues of the PVP/VA copolymer (N-vinylpyrrolidone and vinyl acetate). The predicted solubilities at 25 °C varied considerably depending on the method used. However, the three thermal analysis methods ranked the predicted solubilities in the same order, except for the felodipine−PVP system. Furthermore, the magnitude of the predicted solubilities from the recrystallization method and melting point depression method correlated well with the estimates based on the solubility in the liquid analogues, which suggests that this method can be used as an initial screening tool if a liquid analogue is available. The learnings of this important comparative study provided general guidance for the selection of the most suitable method(s) for the screening of drug−polymer solubility.

Finite Element Analysis of the Enhanced Strength of Laterally Restrained RC Slabs

This paper presents the numerical simulation of the ultimate behaviour of 85 one-way and two-way spanning laterally restrained concrete slabs of variable thickness, span, reinforcement ratio, strength and boundary conditions reported in literature by different authors. The developed numerical model was described and all the assumptions were illustrated. ABAQUS, a Finite Element Analysis suite of software, was employed. Non-linear implicit static general analysis method offered by ABAQUS was used. Other analysis methods were also discussed in general in terms of application such as Explicit Dynamic Analysis and Riks method. The aim is to demonstrate the ability and efficacy of FEA to simulate the ultimate load behaviour of slabs considering different material properties and boundary conditions. The authors intended to present a numerical model that provides consistent predictions of the ultimate behaviour of laterally restrained slabs that could be used as an alternative for expensive real life testing as well as for the design and assessment of new and existing structures respectively. The enhanced strength of laterally-restrained slabs compared with conventional design methods predictions is believed to be due to compressive membrane action (CMA). CMA is an inherent phenomenon of laterally restrained concrete beams/slabs. The numerical predictions obtained from the developed model were in good correlation with the experimental results and with those obtained from the CMA method developed at the Queen’s University Belfast, UK.

How individual participant data meta-analyses have influenced trial design, conduct, and analysis

OBJECTIVES: To demonstrate how individual participant data (IPD) meta-analyses have impacted directly on the design and conduct of trials and highlight other advantages IPD might offer.

STUDY DESIGN AND SETTING: Potential examples of the impact of IPD meta-analyses on trials were identified at an international workshop, attended by individuals with experience in the conduct of IPD meta-analyses and knowledge of trials in their respective clinical areas. Experts in the field who did not attend were asked to provide any further examples. We then examined relevant trial protocols, publications, and Web sites to verify the impacts of the IPD meta-analyses. A subgroup of workshop attendees sought further examples and identified other aspects of trial design and conduct that may inform IPD meta-analyses.

RESULTS: We identified 52 examples of IPD meta-analyses thought to have had a direct impact on the design or conduct of trials. After screening relevant trial protocols and publications, we identified 28 instances where IPD meta-analyses had clearly impacted on trials. They have influenced the selection of comparators and participants, sample size calculations, analysis and interpretation of subsequent trials, and the conduct and analysis of ongoing trials, sometimes in ways that would not possible with systematic reviews of aggregate data. We identified additional potential ways that IPD meta-analyses could be used to influence trials.

CONCLUSIONS: IPD meta-analysis could be better used to inform the design, conduct, analysis, and interpretation of trials.

Integrative analysis of chemo-transcriptomic profiles for drug-feature specific gene expression signatures

One of the major challenges in systems biology is to understand the complex responses of a biological system to external perturbations or internal signalling depending on its biological conditions. Genome-wide transcriptomic profiling of cellular systems under various chemical perturbations allows the manifestation of certain features of the chemicals through their transcriptomic expression profiles. The insights obtained may help to establish the connections between human diseases, associated genes and therapeutic drugs. The main objective of this study was to systematically analyse cellular gene expression data under various drug treatments to elucidate drug-feature specific transcriptomic signatures. We first extracted drug-related information (drug features) from the collected textual description of DrugBank entries using text-mining techniques. A novel statistical method employing orthogonal least square learning was proposed to obtain drug-feature-specific signatures by integrating gene expression with DrugBank data. To obtain robust signatures from noisy input datasets, a stringent ensemble approach was applied with the combination of three techniques: resampling, leave-one-out cross validation, and aggregation. The validation experiments showed that the proposed method has the capacity of extracting biologically meaningful drug-feature-specific gene expression signatures. It was also shown that most of signature genes are connected with common hub genes by regulatory network analysis. The common hub genes were further shown to be related to general drug metabolism by Gene Ontology analysis. Each set of genes has relatively few interactions with other sets, indicating the modular nature of each signature and its drug-feature-specificity. Based on Gene Ontology analysis, we also found that each set of drug feature (DF)-specific genes were indeed enriched in biological processes related to the drug feature. The results of these experiments demonstrated the pot- ntial of the method for predicting certain features of new drugs using their transcriptomic profiles, providing a useful methodological framework and a valuable resource for drug development and characterization.

STPA-SafeSec: Safety and Security Analysis for Cyber-Physical Systems

Cyber-physical systems tightly integrate physical processes and information and communication technologies. As today’s critical infrastructures, e.g., the power grid or water distribution networks, are complex cyber-physical systems, ensuring their safety and security becomes of paramount importance. Traditional safety analysis methods, such as HAZOP, are ill-suited to assess these systems. Furthermore, cybersecurity vulnerabilities are often not considered critical, because their effects on the physical processes are not fully understood. In this work, we present STPA-SafeSec, a novel analysis methodology for both safety and security. Its results show the dependencies between cybersecurity vulnerabilities and system safety. Using this information, the most effective mitigation strategies to ensure safety and security of the system can be readily identified. We apply STPA-SafeSec to a use case in the power grid domain, and highlight its benefits.

Insulin Dependant Diabetes Mellitus: Implications for Male Reproductive Function.

BACKGROUND Diabetes mellitus (DM) is increasing in men of reproductive age. Despite this, the prevalence of diabetes in men attending fertility clinics is largely unknown. Furthermore, studies examining the effects of DM on sperm fertility potential have been limited to conventional semen analysis. METHODS Conventional semen analysis (semen volume, sperm count, motility and morphology) was performed for 27 diabetic (mean age 34 +/- 2 years) and 29 non-diabetic subjects (control group, men undergoing routine infertility investigations, mean age 33 +/- 1 years). Nuclear DNA (nDNA) fragmentation was assessed using the alkaline Comet assay and mitochondrial DNA (mtDNA) deletions by Long-PCR. RESULTS Other than a small, but significant, reduction in semen volume in diabetic men (2.6 versus 3.3 ml; P <0.05), conventional semen parameters did not differ significantly from control subjects. Diabetic subjects had significantly higher mean nDNA fragmentation (53 versus 32%; P <0.0001) and median number of mtDNA deletions (4 versus 3; P <0.05) compared with control subjects. CONCLUSIONS Diabetes is associated with increased sperm nuclear and mtDNA damage that may impair the reproductive capability of these men.

Prediction of adipose tissue composition using Raman spectroscopy: average properties and individual fatty acids.

Abstract: Raman spectroscopy has been used for the first time to predict the FA composition of unextracted adipose tissue of pork, beef, lamb, and chicken. It was found that the bulk unsaturation parameters could be predicted successfully [R-2 = 0.97, root mean square error of prediction (RMSEP) = 4.6% of 4 sigma], with cis unsaturation, which accounted for the majority of the unsaturation, giving similar correlations. The combined abundance of all measured PUFA (>= 2 double bonds per chain) was also well predicted with R-2 = 0.97 and RMSEP = 4.0% of 4 sigma. Trans unsaturation was not as well modeled (R-2 = 0.52, RMSEP = 18% of 4 sigma); this reduced prediction ability can be attributed to the low levels of trans FA found in adipose tissue (0.035 times the cis unsaturation level). For the individual FA, the average partial least squares (PLS) regression coefficient of the 18 most abundant FA (relative abundances ranging from 0.1 to 38.6% of the total FA content) was R-2 = 0.73; the average RMSEP = 11.9% of 4 sigma. Regression coefficients and prediction errors for the five most abundant FA were all better than the average value (in some cases as low as RMSEP = 4.7% of 4 sigma). Cross-correlation between the abundances of the minor FA and more abundant acids could be determined by principal component analysis methods, and the resulting groups of correlated compounds were also well-predicted using PLS. The accuracy of the prediction of individual FA was at least as good as other spectroscopic methods, and the extremely straightforward sampling method meant that very rapid analysis of samples at ambient temperature was easily achieved. This work shows that Raman profiling of hundreds of samples per day is easily achievable with an automated sampling system.

Heritability of immune function in the caterpillar Spodoptera littoralis

Phenoloxidase (PO) is believed to be a key mediator of immune function in insects and has been implicated both in non-self recognition and in resistance to a variety of parasites and pathogens, including baculoviruses and parasitoids. Using larvae of the Egyptian cotton leafworm, Spodoptera littoralis, we found that despite its apparent importance, haemolymph PO activity varied markedly between individuals, even amongst insects reared under apparently identical conditions. Sib-analysis methods were used to determine whether individuals varied genetically in their PO activity, and hence in one aspect of immune function. The heritability estimate of haemolymph PO activity was high (h 2 = 0.690 +/- 0.069), and PO activity in the haemolymph was strongly correlated with PO activity in both the cuticle and midgut; the sites of entry for most parasites and pathogens. Haemolymph PO activity was also strongly correlated with the degree to which a synthetic parasite (a small piece of nylon monofilament) was encapsulated and melanized (r = 0.622 +/- 0.142), suggesting that the encapsulation response is also heritable. The mechanism maintaining this genetic variation has yet to be elucidated.

Genetic differentiation across the social transition in a socially polymorphic sweat bee, Halictus rubicundus

Eusociality is widely considered a major evolutionary transition. The socially polymorphic sweat bee Halictus rubicundus, solitary in cooler regions of its holarctic range and eusocial in warmer parts, is an excellent model organism to address this transition, and specifically the question of whether sociality is associated with a strong barrier to gene flow between phenotypically divergent populations. Mitochondrial DNA (COI) from specimens collected across the British Isles, where both solitary and social phenotypes are represented, displayed limited variation, but placed all specimens in the same European lineage; haplotype network analysis failed to differentiate solitary and social lineages. Microsatellite genetic variability was high and enabled us to quantify genetic differentiation among populations and social phenotypes across Great Britain and Ireland. Results from conceptually different analyses consistently showed greater genetic differentiation between geographically distant populations, independently of their social phenotype, suggesting that the two social forms are not reproductively isolated. A landscape genetic approach revealed significant isolation by distance (Mantel test r = 0.622, p

NON-GAUSSIAN STATISTICS OF OIL PRICING TIME-SERIES: A CASE STUDY

The stochastic nature of oil price fluctuations is investigated over a twelve-year period, borrowing feedback from an existing database (USA Energy Information Administration database, available online). We evaluate the scaling exponents of the fluctuations by employing different statistical analysis methods, namely rescaled range analysis (R/S), scale windowed variance analysis (SWV) and the generalized Hurst exponent (GH) method. Relying on the scaling exponents obtained, we apply a rescaling procedure to investigate the complex characteristics of the probability density functions (PDFs) dominating oil price fluctuations. It is found that PDFs exhibit scale invariance, and in fact collapse onto a single curve when increments are measured over microscales (typically less than 30 days). The time evolution of the distributions is well fitted by a Levy-type stable distribution. The relevance of a Levy distribution is made plausible by a simple model of nonlinear transfer. Our results also exhibit a degree of multifractality as the PDFs change and converge toward to a Gaussian distribution at the macroscales.

Activated oncogenic pathways and therapeutic targets in extranodal nasal-type NK/T cell lymphoma revealed by gene expression profiling

We performed comprehensive genome-wide gene expression profiling (GEP) of extranodal nasal-type natural killer/T-cell lymphoma (NKTL) using formalin-fixed, paraffin-embedded tissue (n = 9) and NK cell lines (n = 5) in comparison with normal NK cells, with the objective of understanding the oncogenic pathways involved in the pathogenesis of NKTL and to identify potential therapeutic targets. Pathway and network analysis of genes differentially expressed between NKTL and normal NK cells revealed significant enrichment for cell cycle-related genes and pathways, such as PLK1, CDK1, and Aurora-A. Furthermore, our results demonstrated a pro-proliferative and anti-apoptotic phenotype in NKTL characterized by activation of Myc and nuclear factor kappa B (NF-kappa B), and deregulation of p53. In corroboration with GEP findings, a significant percentage of NKTLs (n = 33) overexpressed c-Myc (45.4%), p53 (87.9%), and NF-kappa B p50 (67.7%) on immunohistochemistry using a tissue microarray containing 33 NKTL samples. Notably, overexpression of survivin was observed in 97% of cases. Based on our findings, we propose a model of NKTL pathogenesis where deregulation of p53 together with activation of Myc and NF-kappa B, possibly driven by EBV LMP-1, results in the cumulative up-regulation of survivin. Down-regulation of survivin with Terameprocol (EM-1421, a survivin inhibitor) results in reduced cell viability and increased apoptosis in tumour cells, suggesting that targeting survivin may be a potential novel therapeutic strategy in NKTL. Copyright (C) 2011 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.