PointCloudExplore 2: Visual exploration of 3D gene expression

To better understand how developmental regulatory networks are defined inthe genome sequence, the Berkeley Drosophila Transcription Network Project (BDNTP)has developed a suite of methods to describe 3D gene expression data, i.e.,the output of the network at cellular resolution for multiple time points. To allow researchersto explore these novel data sets we have developed PointCloudXplore (PCX).In PCX we have linked physical and information visualization views via the concept ofbrushing (cell selection). For each view dedicated operations for performing selectionof cells are available. In PCX, all cell selections are stored in a central managementsystem. Cells selected in one view can in this way be highlighted in any view allowingfurther cell subset properties to be determined. Complex cell queries can be definedby combining different cell selections using logical operations such as AND, OR, andNOT. Here we are going to provide an overview of PointCloudXplore 2 (PCX2), thelatest publicly available version of PCX. PCX2 has shown to be an effective tool forvisual exploration of 3D gene expression data. We discuss (i) all views available inPCX2, (ii) different strategies to perform cell selection, (iii) the basic architecture ofPCX2., and (iv) illustrate the usefulness of PCX2 using selected examples

NMR Methods to Study Dynamic Allostery

Nuclear magnetic resonance (NMR) spectroscopy provides a unique toolbox of experimental probes for studying dynamic processes on a wide range of timescales, ranging from picoseconds to milliseconds and beyond. Along with NMR hardware developments, recent methodological advancements have enabled the characterization of allosteric proteins at unprecedented detail, revealing intriguing aspects of allosteric mechanisms and increasing the proportion of the conformational ensemble that can be observed by experiment. Here, we present an overview of NMR spectroscopic methods for characterizing equilibrium fluctuations in free and bound states of allosteric proteins that have been most influential in the field. By combining NMR experimental approaches with molecular simulations, atomistic-level descriptions of the mechanisms by which allosteric phenomena take place are now within reach

Separable Roles for a Caenorhabditis elegans RMI1 Homolog in Promoting and Antagonizing Meiotic Crossovers Ensure Faithful Chromosome Inheritance

During the first meiotic division, crossovers (COs) between homologous chromosomes ensure their correct segregation. COs are produced by homologous recombination (HR)-mediated repair of programmed DNA double strand breaks (DSBs). As more DSBs are induced than COs, mechanisms are required to establish a regulated number of COs and to repair remaining intermediates as non-crossovers (NCOs). We show that the Caenorhabditis elegans RMI1 homolog-1 (RMH-1) functions during meiosis to promote both CO and NCO HR at appropriate chromosomal sites. RMH-1 accumulates at CO sites, dependent on known pro-CO factors, and acts to promote CO designation and enforce the CO outcome of HR-intermediate resolution. RMH-1 also localizes at NCO sites and functions in parallel with SMC-5 to antagonize excess HR-based connections between chromosomes. Moreover, RMH-1 also has a major role in channeling DSBs into an NCO HR outcome near the centers of chromosomes, thereby ensuring that COs form predominantly at off-center positions

Packing topology in crystals of proteins and small molecules: a comparison

We compared the topologies of protein and small molecule crystals, which have many common features – both are molecular crystals with intermolecular interactions much weaker than intramolecular interactions. They also have different features – a considerably large fraction of the volume of protein crystals is occupied by liquid water while no room is available to other molecules in small molecule crystals. We analyzed the overall and local topology and performed multilevel topological analyses (with the software package ToposPro) of carefully selected high quality sets of protein and small molecule crystal structures. Given the suboptimal packing of protein crystals, which is due the special shape and size of proteins, it would be reasonable to expect that the topology of protein crystals is different from the topology of small molecule crystals. Surprisingly, we discovered that these two types of crystalline compounds have strikingly similar topologies. This might suggest that molecular crystal formations share symmetry rules independent of molecular dimension.© The Author(s) 201

The effects of probe binding affinity differences on gene expression measurements and how to deal with them

MOTIVATION: When comparing gene expression levels between species or strains using microarrays, sequence differences between the groups can cause false identification of expression differences. Our simulated dataset shows that a sequence divergence of only 1% between species can lead to falsely reported expression differences for >50% of the transcripts-similar levels of effect have been reported previously in comparisons of human and chimpanzee expression. We propose a method for identifying probes that cause such false readings, using only the microarray data, so that problematic probes can be excluded from analysis. We then test the power of the method to detect sequence differences and to correct for falsely reported expression differences. Our method can detect 70% of the probes with sequence differences using human and chimpanzee data, while removing only 18% of probes with no sequence differences. Although only 70% of the probes with sequence differences are detected, the effect of removing probes on falsely reported expression differences is more dramatic: the method can remove 98% of the falsely reported expression differences from a simulated dataset. We argue that the method should be used even when sequence data are available. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Eco-Metabolomics and Metabolic Modeling: Making the Leap From Model Systems in the Lab to Native Populations in the Field

Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.© 2018 Nagler, Nägele, Gilli, Fragner, Korte, Platzer, Farlow, Nordborg and Weckwert

Decreased expression of endogenous feline leukemia virus in cat lymphomas: a case control study

Background: Cats infected with exogenous feline leukemia virus (exFeLV) have a higher chance of lymphoma development than uninfected cats. Furthermore, an increased exFeLV transcription has been detected in lymphomas compared to non-malignant tissues. The possible mechanisms of lymphoma development by exFeLV are insertional mutagenesis or persistent stimulation of host immune cells by viral antigens, bringing them at risk for malignant transformation. Vaccination of cats against exFeLV has in recent years decreased the overall infection rate in most countries. Nevertheless, an increasing number of lymphomas have been diagnosed among exFeLV-negative cats. Endogenous feline leukemia virus (enFeLV) is another retrovirus for which transcription has been observed in cat lymphomas. EnFeLV provirus elements are present in the germline of various cat species and share a high sequence similarity with exFeLV but, due to mutations, are incapable of producing infectious viral particles. However, recombination between exFeLV and enFeLV could produce infectious particles. Results: We examined the FeLV expression in cats that have developed malignant lymphomas and discussed the possible mechanisms that could have induced malignant transformation. For expression analysis we used next-generation RNA-sequencing (RNA-Seq) and for validation reverse transcription quantitative PCR (RT-qPCR). First, we showed that there was no expression of exFeLV in all samples, which eliminates the possibility of recombination between exFeLV and enFeLV. Next, we analyzed the difference in expression of three enFeLV genes between control and lymphoma samples. Our analysis showed an average of 3.40-fold decreased viral expression for the three genes in lymphoma compared to control samples. The results were confirmed by RT-qPCR. Conclusions: There is a decreased expression of enFeLV genes in lymphomas versus control samples, which contradicts previous observations for the exFeLV. Our results suggest that a persistent stimulation of host immune cells is not an appropriate mechanism responsible for malignant transformation caused by feline endogenous retroviruses

PointCloudExplore 2: Visual exploration of 3D gene expression

To better understand how developmental regulatory networks are defined in the genome sequence, the Berkeley Drosophila Transcription Network Project (BDNTP) has developed a suite of methods to describe 3D gene expression data, i.e., the output of the network at cellular resolution for multiple time points. To allow researchers to explore these novel data sets we have developed PointCloudXplore (PCX). In PCX we have linked physical and information visualization views via the concept of brushing (cell selection). For each view dedicated operations for performing selection of cells are available. In PCX, all cell selections are stored in a central management system. Cells selected in one view can in this way be highlighted in any view allowing further cell subset properties to be determined. Complex cell queries can be defined by combining different cell selections using logical operations such as AND, OR, and NOT. Here we are going to provide an overview of PointCloudXplore 2 (PCX2), the latest publicly available version of PCX. PCX2 has shown to be an effective tool for visual exploration of 3D gene expression data. We discuss (i) all views available in PCX2, (ii) different strategies to perform cell selection, (iii) the basic architecture of PCX2., and (iv) illustrate the usefulness of PCX2 using selected examples

Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia

BACKGROUND: Many mutations that contribute to the pathogenesis of acute myeloid leukemia (AML) are undefined. The relationships between patterns of mutations and epigenetic phenotypes are not yet clear. METHODS: We analyzed the genomes of 200 clinically annotated adult cases of de novo AML, using either whole-genome sequencing (50 cases) or whole-exome sequencing (150 cases), along with RNA and microRNA sequencing and DNA-methylation analysis. RESULTS: AML genomes have fewer mutations than most other adult cancers, with an average of only 13 mutations found in genes. Of these, an average of 5 are in genes that are recurrently mutated in AML. A total of 23 genes were significantly mutated, and another 237 were mutated in two or more samples. Nearly all samples had at least 1 nonsynonymous mutation in one of nine categories of genes that are almost certainly relevant for pathogenesis, including transcription-factor fusions (18% of cases), the gene encoding nucleophosmin (NPM1) (27%), tumor-suppressor genes (16%), DNA-methylation-related genes (44%), signaling genes (59%), chromatin-modifying genes (30%), myeloid transcription-factor genes (22%), cohesin-complex genes (13%), and spliceosome-complex genes (14%). Patterns of cooperation and mutual exclusivity suggested strong biologic relationships among several of the genes and categories. CONCLUSIONS: We identified at least one potential driver mutation in nearly all AML samples and found that a complex interplay of genetic events contributes to AML pathogenesis in individual patients. The databases from this study are widely available to serve as a foundation for further investigations of AML pathogenesis, classification, and risk stratification. (Funded by the National Institutes of Health.) Copyright © 2013 Massachusetts Medical Society