Incidence of WHO stage 3 and 4 conditions following initiation of Anti-Retroviral Therapy in resource limited settings

To determine the incidence of WHO clinical stage 3 and 4 conditions during early anti-retroviral therapy (ART) in resource limited settings (RLS)

Evaluation of multi-exponential curve fitting analysis of oxygen-quenched phosphorescence decay traces for recovering microvascular oxygen tension histograms

Although it is generally accepted that oxygen-quenched phosphorescence decay traces can be analyzed using the exponential series method (ESM), its application until now has been limited to a few (patho)physiological studies, probably because the reliability of the recovered oxygen tension (pO2) histograms has never been extensively evaluated and lacks documentation. The aim of this study was, therefore, to evaluate the use of the ESM to adequately determine pO2 histograms from phosphorescence decay traces. For this purpose we simulated decay traces corresponding to uni- and bimodal pO2 distributions and recovered the pO2 histograms at different signal-to-noise ratios (SNRs). Ultimately, we recovered microvascular pO2 histograms measured in the rat kidney in a model of endotoxemic shock and fluid resuscitation and showed that the mean microvascular oxygen tension, 〈pO2〉, decreased after induction of endotoxemia and that after 2 h of fluid resuscitation, 〈pO2〉 remained low, but the hypoxic peak that had arisen during endotoxemia was reduced. This finding illustrates the importance of recovering pO2 histograms under (patho)physiological conditions. In conclusion, this study has characterized how noise affects the recovery of pO2 histograms using the ESM and documented the reliability of the ESM for recovering both low- and high-pO2 distributions for SNRs typically found in experiments. This study might therefore serve as a frame of reference for investigations focused on oxygen (re)distribution during health and disease and encourage researchers to (re-)analyze data obtained in (earlier) studies possibly revealing new insights into complex disease states and treatment strategies

The CDK inhibitor CR8 acts as a molecular glue degrader that depletes cyclin K

Molecular glue compounds induce protein-protein interactions that, in the context of a ubiquitin ligase, lead to protein degradation1. Unlike traditional enzyme inhibitors, these molecular glue degraders act substoichiometrically to catalyse the rapid depletion of previously inaccessible targets2. They are clinically effective and highly sought-after, but have thus far only been discovered serendipitously. Here, through systematically mining databases for correlations between the cytotoxicity of 4,518 clinical and preclinical small molecules and the expression levels of E3 ligase components across hundreds of human cancer cell lines3-5, we identify CR8-a cyclin-dependent kinase (CDK) inhibitor6-as a compound that acts as a molecular glue degrader. The CDK-bound form of CR8 has a solvent-exposed pyridyl moiety that induces the formation of a complex between CDK12-cyclin K and the CUL4 adaptor protein DDB1, bypassing the requirement for a substrate receptor and presenting cyclin K for ubiquitination and degradation. Our studies demonstrate that chemical alteration of surface-exposed moieties can confer gain-of-function glue properties to an inhibitor, and we propose this as a broader strategy through which target-binding molecules could be converted into molecular glues

Molecular classification of selective oestrogen receptor modulators on the basis of gene expression profiles of breast cancer cells expressing oestrogen receptor α

The purpose of this study was to classify selective oestrogen receptor modulators based on gene expression profiles produced in breast cancer cells expressing either wtERα or mutant351ERα. In total, 54 microarray experiments were carried out by using a commercially available Atlas cDNA Expression Arrays (Clontech), containing 588 cancer-related genes. Nine sets of data were generated for each cell line following 24 h of treatment: expression data were obtained for cells treated with vehicle EtOH (Control); with 10−9 or 10−8 M oestradiol; with 10−6 M 4-hydroxytamoxifen; with 10−6 M raloxifene; with 10−6 M idoxifene, with 10−6 M EM 652, with 10−6 M GW 7604; with 5×10−5 M resveratrol and with 10−6 M ICI 182,780. We developed a new algorithm ‘Expression Signatures’ to classify compounds on the basis of differential gene expression profiles. We created dendrograms for each cell line, in which branches represent relationships between compounds. Additionally, clustering analysis was performed using different subsets of genes to assess the robustness of the analysis. In general, only small differences between gene expression profiles treated with compounds were observed with correlation coefficients ranged from 0.83 to 0.98. This observation may be explained by the use of the same cell context for treatments with compounds that essentially belong to the same class of drugs with oestrogen receptors related mechanisms. The most surprising observation was that ICI 182,780 clustered together with oestrodiol and raloxifene for cells expressing wtERα and clustered together with EM 652 for cells expressing mutant351ERα. These data provide a rationale for a more precise and elaborate study in which custom made oligonucleotide arrays can be used with comprehensive sets of genes known to have consensus and putative oestrogen response elements in their promoter regions

Detection of regulator genes and eQTLs in gene networks

Genetic differences between individuals associated to quantitative phenotypic traits, including disease states, are usually found in non-coding genomic regions. These genetic variants are often also associated to differences in expression levels of nearby genes (they are "expression quantitative trait loci" or eQTLs for short) and presumably play a gene regulatory role, affecting the status of molecular networks of interacting genes, proteins and metabolites. Computational systems biology approaches to reconstruct causal gene networks from large-scale omics data have therefore become essential to understand the structure of networks controlled by eQTLs together with other regulatory genes, and to generate detailed hypotheses about the molecular mechanisms that lead from genotype to phenotype. Here we review the main analytical methods and softwares to identify eQTLs and their associated genes, to reconstruct co-expression networks and modules, to reconstruct causal Bayesian gene and module networks, and to validate predicted networks in silico.Comment: minor revision with typos corrected; review article; 24 pages, 2 figure

Personalized medicine in psoriasis: developing a genomic classifier to predict histological response to Alefacept

<p>Abstract</p> <p>Background</p> <p>Alefacept treatment is highly effective in a select group patients with moderate-to-severe psoriasis, and is an ideal candidate to develop systems to predict who will respond to therapy. A clinical trial of 22 patients with moderate to severe psoriasis treated with alefacept was conducted in 2002-2003, as a mechanism of action study. Patients were classified as responders or non-responders to alefacept based on histological criteria. Results of the original mechanism of action study have been published. Peripheral blood was collected at the start of this clinical trial, and a prior analysis demonstrated that gene expression in PBMCs differed between responders and non-responders, however, the analysis performed could not be used to predict response.</p> <p>Methods</p> <p>Microarray data from PBMCs of 16 of these patients was analyzed to generate a treatment response classifier. We used a discriminant analysis method that performs sample classification from gene expression data, via "nearest shrunken centroid method". Centroids are the average gene expression for each gene in each class divided by the within-class standard deviation for that gene.</p> <p>Results</p> <p>A disease response classifier using 23 genes was created to accurately predict response to alefacept (12.3% error rate). While the genes in this classifier should be considered as a group, some of the individual genes are of great interest, for example, cAMP response element modulator (CREM), v-MAF avian musculoaponeurotic fibrosarcoma oncogene family (MAFF), chloride intracellular channel protein 1 (CLIC1, also called NCC27), NLR family, pyrin domain-containing 1 (NLRP1), and CCL5 (chemokine, cc motif, ligand 5, also called regulated upon activation, normally T expressed, and presumably secreted/RANTES).</p> <p>Conclusions</p> <p>Although this study is small, and based on analysis of existing microarray data, we demonstrate that a treatment response classifier for alefacept can be created using gene expression of PBMCs in psoriasis. This preliminary study may provide a useful tool to predict response of psoriatic patients to alefacept.</p

Array algorithms for H^2 and H^∞ estimation

Currently, the preferred method for implementing H^2 estimation algorithms is what is called the array form, and includes two main families: square-root array algorithms, that are typically more stable than conventional ones, and fast array algorithms, which, when the system is time-invariant, typically offer an order of magnitude reduction in the computational effort. Using our recent observation that H^∞ filtering coincides with Kalman filtering in Krein space, in this chapter we develop array algorithms for H^∞ filtering. These can be regarded as natural generalizations of their H^2 counterparts, and involve propagating the indefinite square roots of the quantities of interest. The H^∞ square-root and fast array algorithms both have the interesting feature that one does not need to explicitly check for the positivity conditions required for the existence of H^∞ filters. These conditions are built into the algorithms themselves so that an H^∞ estimator of the desired level exists if, and only if, the algorithms can be executed. However, since H^∞ square-root algorithms predominantly use J-unitary transformations, rather than the unitary transformations required in the H^2 case, further investigation is needed to determine the numerical behavior of such algorithms

Coral Reef Fish Rapidly Learn to Identify Multiple Unknown Predators upon Recruitment to the Reef

Organisms often undergo shifts in habitats as their requirements change with ontogeny

Multiway modeling and analysis in stem cell systems biology

<p>Abstract</p> <p>Background</p> <p>Systems biology refers to multidisciplinary approaches designed to uncover emergent properties of biological systems. Stem cells are an attractive target for this analysis, due to their broad therapeutic potential. A central theme of systems biology is the use of computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, etc.). A number of deterministic, probabilistic, and statistical learning models are used to understand sophisticated cellular behaviors such as protein expression during cellular differentiation and the activity of signaling networks. However, many of these models are bimodal i.e., they only consider row-column relationships. In contrast, multiway modeling techniques (also known as tensor models) can analyze multimodal data, which capture much more information about complex behaviors such as cell differentiation. In particular, tensors can be very powerful tools for modeling the dynamic activity of biological networks over time. Here, we review the application of systems biology to stem cells and illustrate application of tensor analysis to model collagen-induced osteogenic differentiation of human mesenchymal stem cells.</p> <p>Results</p> <p>We applied Tucker1, Tucker3, and Parallel Factor Analysis (PARAFAC) models to identify protein/gene expression patterns during extracellular matrix-induced osteogenic differentiation of human mesenchymal stem cells. In one case, we organized our data into a tensor of type protein/gene locus link × gene ontology category × osteogenic stimulant, and found that our cells expressed two distinct, stimulus-dependent sets of functionally related genes as they underwent osteogenic differentiation. In a second case, we organized DNA microarray data in a three-way tensor of gene IDs × osteogenic stimulus × replicates, and found that application of tensile strain to a collagen I substrate accelerated the osteogenic differentiation induced by a static collagen I substrate.</p> <p>Conclusion</p> <p>Our results suggest gene- and protein-level models whereby stem cells undergo transdifferentiation to osteoblasts, and lay the foundation for mechanistic, hypothesis-driven studies. Our analysis methods are applicable to a wide range of stem cell differentiation models.</p

Geometric methods on low-rank matrix and tensor manifolds

In this chapter we present numerical methods for low-rank matrix and tensor problems that explicitly make use of the geometry of rank constrained matrix and tensor spaces. We focus on two types of problems: The first are optimization problems, like matrix and tensor completion, solving linear systems and eigenvalue problems. Such problems can be solved by numerical optimization for manifolds, called Riemannian optimization methods. We will explain the basic elements of differential geometry in order to apply such methods efficiently to rank constrained matrix and tensor spaces. The second type of problem is ordinary differential equations, defined on matrix and tensor spaces. We show how their solution can be approximated by the dynamical low-rank principle, and discuss several numerical integrators that rely in an essential way on geometric properties that are characteristic to sets of low rank matrices and tensors