Detection of gene pathways with predictive power for breast cancer prognosis

Mitochondrion-to-Chloroplast DNA Transfers and Intragenomic Proliferation of Chloroplast Group II Introns in Gloeotilopsis Green Algae (Ulotrichales, Ulvophyceae)

To probe organelle genome evolution in the Ulvales/Ulotrichales clade, the newly sequenced chloroplast and mitochondrial genomes of Gloeotilopsis planctonica and Gloeotilopsis sarcinoidea (Ulotrichales) were compared with those of Pseudendoclonium akinetum (Ulotrichales) and of the few other green algae previously sampled in the Ulvophyceae. At 105,236 bp, the G planctonica mitochondrial DNA (mtDNA) is the largest mitochondrial genome reported so far among chlorophytes, whereas the 221,431-bp G planctonica and 262,888-bp G sarcinoidea chloroplast DNAs (cpDNAs) are the largest chloroplast genomes analyzed among the Ulvophyceae. Gains of non-coding sequences largely account for the expansion of these genomes. Both Gloeotilopsis cpDNAs lack the inverted repeat (IR) typically found in green plants, indicating that two independent IR losses occurred in the Ulvales/Ulotrichales. Our comparison of the Pseudendoclonium and Gloeotilopsis cpDNAs offered clues regarding the mechanism of IR loss in the Ulotrichales, suggesting that internal sequences from the rDNA operon were differentially lost from the two original IR copies during this process. Our analyses also unveiled a number of genetic novelties. Short mtDNA fragments were discovered in two distinct regions of the G sarcinoidea cpDNA, providing the first evidence for intracellular inter-organelle gene migration in green algae. We identified for the first time in green algal organelles, group II introns with LAGLIDADG ORFs as well as group II introns inserted into untranslated gene regions. We discovered many group II introns occupying sites not previously documented for the chloroplast genome and demonstrated that a number of them arose by intragenomic proliferation, most likely through retrohoming.

Extensive complementarity between gene function prediction methods

MOTIVATION

The number of sequenced genomes rises steadily but we still lack the knowledge about the biological roles of many genes. Automated function prediction (AFP) is thus a necessity. We hypothesized that AFP approaches that draw on distinct genome features may be useful for predicting different types of gene functions, motivating a systematic analysis of the benefits gained by obtaining and integrating such predictions.

RESULTS

Our pipeline amalgamates 5 133 543 genes from 2071 genomes in a single massive analysis that evaluates five established genomic AFP methodologies. While 1227 Gene Ontology (GO) terms yielded reliable predictions, the majority of these functions were accessible to only one or two of the methods. Moreover, different methods tend to assign a GO term to non-overlapping sets of genes. Thus, inferences made by diverse genomic AFP methods display a striking complementary, both gene-wise and function-wise. Because of this, a viable integration strategy is to rely on a single most-confident prediction per gene/function, rather than enforcing agreement across multiple AFP methods. Using an information-theoretic approach, we estimate that current databases contain 29.2 bits/gene of known Escherichia coli gene functions. This can be increased by up to 5.5 bits/gene using individual AFP methods or by 11 additional bits/gene upon integration, thereby providing a highly-ranking predictor on the Critical Assessment of Function Annotation 2 community benchmark. Availability of more sequenced genomes boosts the predictive accuracy of AFP approaches and also the benefit from integrating them.

AVAILABILITY AND IMPLEMENTATION

The individual and integrated GO predictions for the complete set of genes are available from http://gorbi.irb.hr/ CONTACT: fran.supek@irb.hrSupplementary information: Supplementary materials are available at Bioinformatics online.

Prediction of FAD binding sites in electron transport proteins according to efficient radial basis function networks and significant amino acid pairs

BACKGROUND

Cellular respiration is a catabolic pathway for producing adenosine triphosphate (ATP) and is the most efficient process through which cells harvest energy from consumed food. When cells undergo cellular respiration, they require a pathway to keep and transfer electrons (i.e., the electron transport chain). Due to oxidation-reduction reactions, the electron transport chain produces a transmembrane proton electrochemical gradient. In case protons flow back through this membrane, this mechanical energy is converted into chemical energy by ATP synthase. The convert process is involved in producing ATP which provides energy in a lot of cellular processes. In the electron transport chain process, flavin adenine dinucleotide (FAD) is one of the most vital molecules for carrying and transferring electrons. Therefore, predicting FAD binding sites in the electron transport chain is vital for helping biologists understand the electron transport chain process and energy production in cells.

RESULTS

We used an independent data set to evaluate the performance of the proposed method, which had an accuracy of 69.84 %. We compared the performance of the proposed method in analyzing two newly discovered electron transport protein sequences with that of the general FAD binding predictor presented by Mishra and Raghava and determined that the accuracy of the proposed method improved by 9-45 % and its Matthew's correlation coefficient was 0.14-0.5. Furthermore, the proposed method enabled reducing the number of false positives significantly and can provide useful information for biologists.

CONCLUSIONS

We developed a method that is based on PSSM profiles and SAAPs for identifying FAD binding sites in newly discovered electron transport protein sequences. This approach achieved a significant improvement after we added SAAPs to PSSM features to analyze FAD binding proteins in the electron transport chain. The proposed method can serve as an effective tool for predicting FAD binding sites in electron transport proteins and can help biologists understand the functions of the electron transport chain, particularly those of FAD binding sites. We also developed a web server which identifies FAD binding sites in electron transporters available for academics.

Type IV pili promote early biofilm formation by Clostridium difficile

Increasing morbidity and mortality from Clostridium difficile infection (CDI) present an enormous challenge to healthcare systems. Clostridium difficile express type IV pili (T4P), but their function remains unclear. Many chronic and recurrent bacterial infections result from biofilms, surface-associated bacterial communities embedded in an extracellular matrix. CDI may be biofilm mediated; T4P are important for biofilm formation in a number of organisms. We evaluate the role of T4P in C. difficile biofilm formation using RNA sequencing, mutagenesis and complementation of the gene encoding the major pilin pilA1, and microscopy. RNA sequencing demonstrates that, in comparison to other growth phenotypes, C. difficile growing in a biofilm has a distinct RNA expression profile, with significant differences in T4P gene expression. Microscopy of T4P-expressing and T4P-deficient strains suggests that T4P play an important role in early biofilm formation. A non-piliated pilA1 mutant forms an initial biofilm of significantly reduced mass and thickness in comparison to the wild type. Complementation of the pilA1 mutant strain leads to formation of a biofilm which resembles the wild-type biofilm. These findings suggest that T4P play an important role in early biofilm formation. Novel strategies for confronting biofilm infections are emerging; our data suggest that similar strategies should be investigated in CDI.

CellProfiler Analyst: interactive data exploration, analysis and classification of large biological image sets

CellProfiler Analyst allows the exploration and visualization of image-based data, together with the classification of complex biological phenotypes, via an interactive user interface designed for biologists and data scientists. CellProfiler Analyst 2.0, completely rewritten in Python, builds on these features and adds enhanced supervised machine learning capabilities (Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery).

AVAILABILITY AND IMPLEMENTATION

CellProfiler Analyst 2.0 is free and open source, available at http://www.cellprofiler.org and from GitHub (https://github.com/CellProfiler/CellProfiler-Analyst) under the BSD license. It is available as a packaged application for Mac OS X and Microsoft Windows and can be compiled for Linux. We implemented an automatic build process that supports nightly updates and regular release cycles for the software.

CONTACT

anne@broadinstitute.orgSupplementary information: Supplementary data are available at Bioinformatics online.

UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling

MOTIVATION

Recent experimental studies have suggested that proteins fold via stepwise assembly of structural units named 'foldons' through the process of sequential stabilization. Alongside, latest developments on computational side based on probabilistic modeling have shown promising direction to perform de novo protein conformational sampling from continuous space. However, existing computational approaches for de novo protein structure prediction often randomly sample protein conformational space as opposed to experimentally suggested stepwise sampling.

RESULTS

Here, we develop a novel generative, probabilistic model that simultaneously captures local structural preferences of backbone and side chain conformational space of polypeptide chains in a united-residue representation and performs experimentally motivated conditional conformational sampling via stepwise synthesis and assembly of foldon units that minimizes a composite physics and knowledge-based energy function for de novo protein structure prediction. The proposed method, UniCon3D, has been found to (i) sample lower energy conformations with higher accuracy than traditional random sampling in a small benchmark of 6 proteins; (ii) perform comparably with the top five automated methods on 30 difficult target domains from the 11th Critical Assessment of Protein Structure Prediction (CASP) experiment and on 15 difficult target domains from the 10th CASP experiment; and (iii) outperform two state-of-the-art approaches and a baseline counterpart of UniCon3D that performs traditional random sampling for protein modeling aided by predicted residue-residue contacts on 45 targets from the 10th edition of CASP.

AVAILABILITY AND IMPLEMENTATION

Source code, executable versions, manuals and example data of UniCon3D for Linux and OSX are freely available to non-commercial users at http://sysbio.rnet.missouri.edu/UniCon3D/ CONTACT: chengji@missouri.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Phylogeny-aware identification and correction of taxonomically mislabeled sequences

Molecular sequences in public databases are mostly annotated by the submitting authors without further validation. This procedure can generate erroneous taxonomic sequence labels. Mislabeled sequences are hard to identify, and they can induce downstream errors because new sequences are typically annotated using existing ones. Furthermore, taxonomic mislabelings in reference sequence databases can bias metagenetic studies which rely on the taxonomy. Despite significant efforts to improve the quality of taxonomic annotations, the curation rate is low because of the labor-intensive manual curation process. Here, we present SATIVA, a phylogeny-aware method to automatically identify taxonomically mislabeled sequences (‘mislabels’) using statistical models of evolution. We use the Evolutionary Placement Algorithm (EPA) to detect and score sequences whose taxonomic annotation is not supported by the underlying phylogenetic signal, and automatically propose a corrected taxonomic classification for those. Using simulated data, we show that our method attains high accuracy for identification (96.9% sensitivity/91.7% precision) as well as correction (94.9% sensitivity/89.9% precision) of mislabels. Furthermore, an analysis of four widely used microbial 16S reference databases (Greengenes, LTP, RDP and SILVA) indicates that they currently contain between 0.2% and 2.5% mislabels. Finally, we use SATIVA to perform an in-depth evaluation of alternative taxonomies for Cyanobacteria. SATIVA is freely available at https://github.com/amkozlov/sativa.

mInDel: a high-throughput and efficient pipeline for genome-wide InDel marker development

Background

Rich in genetic information and cost-effective to genotype, the Insertion-Deletion (InDel) molecular marker system is an important tool for studies in genetics, genomics and for marker-assisted breeding. Advent of next-generation sequencing (NGS) revolutionized the speed and throughput of sequence data generation, and enabled genome-wide identification of insertion and deletion variation. However, current NGS-based InDel mining tools, such as Samtools, GATK and Atlas2, all rely on a reference genome for variant calling which hinders their application on unsequenced organisms and the output of short InDels compromised their use on gel-based genotyping platforms. To address these issues, an enhanced platform is needed to identify longer InDels and develop markers in absence of a reference genome.

Results

Here we present mInDel (multiple InDel), a next-generation variant calling tool specifically designed for InDel marker discovery. By taking in raw sequence reads and assembling them into contigs de novo, this software identifies InDel polymorphisms using a sliding window alignment from assembled contigs, rendering a unique advantage when a reference genome is unavailable. By providing an option of combining multiple discovered InDels as output, mInDel is amiable to gel-based genotyping platforms where markers with large polymorphisms are preferred. We demonstrated the usability and performance of this software through a case study using a set of maize NGS data, and experimentally validated the accuracy of markers generated from mInDel.

Conclusions

mInDel is a novel and practical tool that enables rapid genome-wide InDel marker discovery. The features of being independent from a reference genome and the flexibility with downstream genotyping platforms will allow a broad range of applications across genetics research and plant breeding. The mInDel pipeline is freely available at www.github.com/lyd0527/mInDel.

Inhibition of WWP2 suppresses proliferation, and induces G1 cell cycle arrest and apoptosis in liver cancer cells

Primary liver cancer is one of the most common and aggressive human malignancies worldwide. As numerous studies have revealed that WW domain containing E3 Ub‑protein ligase 2 (WWP2) exerts cancer‑specific functions, the present study assessed the role of WWP2 in liver cancer. WWP2 was revealed to be significantly overexpressed in liver cancer tissues compared with paired normal tissues at the mRNA as well as at the protein level. Furthermore, small interfering RNA-mediated WWP2 knockdown in liver cancer cell lines was demonstrated to inhibit cell proliferation, cause cell cycle arrested in G1 phase and to induce apoptosis as revealed by a Cell Counting Kit-8 assay and flow cytometric analysis. In addition, western blot analysis revealed that WWP2 knockdown significantly increased the expression of apoptosis-associated markers caspase‑7, caspase‑8 and B-cell lymphoma 2 (Bcl-2)-associated X in liver cancer cell lines, while Bcl‑2 was significantly decreased. In conclusion, the present study suggested that WWP2 may exert important functions in the over‑proliferation and evasion of apoptosis of liver cancer, likely through regulating the expression of apoptosis-associated markers. Furthermore, WWP2 may represent a novel diagnostic marker and molecular therapeutic target for liver cancer.

Circadian Genes in Breast Cancer

Exploring the putative impact of circadian rhythms is a relatively novel approach to illuminating hormone-related female breast cancer etiology and prognosis. One of several proposed mechanisms underlying breast cancer risk among individuals exposed to light at night involves circadian gene alterations. Although in vitro and animal studies indicate a key role of circadian genes in breast tumor suppression, there is a paucity of data on the role of circadian genes in human breast cancer. This review summarizes recent findings of circadian gene expression and DNA methylation profile from human breast cancer studies in relation to hormonal status, clinicopathological features of tumors, and exposure to night shift work. The major findings from human studies indicate that expression of circadian genes is deregulated in breast cancer. Breast cancer etiology and prognosis-associated PERs, CRYs, CLOCK downregulation, and TIMELESS upregulation may be related to relevant gene methylation in tumor tissue. Alterations and desynchronization of molecular clock machinery found on genetic and epigenetic level were observed in more aggressive breast cancer tumors and those lacking estrogen receptors.

Designing alternative splicing RNA-seq studies. Beyond generic guidelines

MOTIVATION

Designing an RNA-seq study depends critically on its specific goals, technology and underlying biology, which renders general guidelines inadequate. We propose a Bayesian framework to customize experiments so that goals can be attained and resources are not wasted, with a focus on alternative splicing.

RESULTS

We studied how read length, sequencing depth, library preparation and the number of replicates affects cost-effectiveness of single-sample and group comparison studies. Optimal settings varied strongly according to the target organism or tissue (potential 50-500% cost cuts) and, interestingly, short reads outperformed long reads for standard analyses. Our framework learns key characteristics for study design from the data, and predicts if and how to continue experimentation. These predictions matched several follow-up experimental datasets that were used for validation. We provide default pipelines, but the framework can be combined with other data analysis methods and can help assess their relative merits.

AVAILABILITY AND IMPLEMENTATION

casper package at www.bioconductor.org/packages/release/bioc/html/casper.html, Supplementary Manual by typing casperDesign() at the R prompt.

CONTACT

rosselldavid@gmail.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Systems Biology - A Pivotal Research Methodology for Understanding the Mechanisms of Traditional Medicine

Objectives:

Systems biology is a novel subject in the field of life science that aims at a systems’ level understanding of biological systems. Because of the significant progress in high-throughput technologies and molecular biology, systems biology occupies an important place in research during the post-genome era.

Methods:

The characteristics of systems biology and its applicability to traditional medicine research have been discussed from three points of view: data and databases, network analysis and inference, and modeling and systems prediction.

Results:

The existing databases are mostly associated with medicinal herbs and their activities, but new databases reflecting clinical situations and platforms to extract, visualize and analyze data easily need to be constructed. Network pharmacology is a key element of systems biology, so addressing the multi-component, multi-target aspect of pharmacology is important. Studies of network pharmacology highlight the drug target network and network target. Mathematical modeling and simulation are just in their infancy, but mathematical modeling of dynamic biological processes is a central aspect of systems biology. Computational simulations allow structured systems and their functional properties to be understood and the effects of herbal medicines in clinical situations to be predicted.

Conclusion:

Systems biology based on a holistic approach is a pivotal research methodology for understanding the mechanisms of traditional medicine. If systems biology is to be incorporated into traditional medicine, computational technologies and holistic insights need to be integrated.

A Dynamic Programming Algorithm For (1,2)-Exemplar Breakpoint Distance

The exemplar breakpoint distance problem is motivated by finding conserved sets of genes between two genomes. It asks to find respective exemplars in two genomes to minimize the breakpoint distance between them. If one genome has no repeated gene (called trivial genome) and the other has genes repeating at most twice, it is referred to as the (1, 2)-exemplar breakpoint distance problem, EBD(1, 2) for short. Little has been done on algorithm design for this problem by now. In this article, we propose a parameter to describe the maximum physical span between two copies of a gene in a genome, and based on it, design a fixed-parameter algorithm for EBD(1, 2). Using a dynamic programming approach, our algorithm can take O(4(s)n(2)) time and O(4(s)n) space to solve an EBD(1, 2) instance that has two genomes of n genes where the second genome has each two copies of a gene spanning at most s copies of the genes. Our algorithm can also be used to compute the maximum adjacencies between two genomes. The algorithm has been implemented in C++. Simulations on randomly generated data have verified the effectiveness of our algorithm. The software package is available from the authors.

Deodorization of pig slurry and characterization of bacterial diversity using 16S rDNA sequence analysis

The concentration of major odor-causing compounds including phenols, indoles, short-chain fatty acids (SCFAs) and branched chain fatty acids (BCFAs) in response to the addition of powdered horse radish (PHR) and spent mushroom compost (SMC) was compared with control non-treated slurry (CNS) samples. A total of 97,465 rDNAs sequence reads were generated from three different samples (CNS, n = 2; PHR, n = 3; SMC, n = 3) using bar-coded pyrosequencing. The number of operational taxonomic units (OTUs) was lower in the PHR slurry compared with the other samples. A total of 11 phyla were observed in the slurry samples, while the phylogenetic analysis revealed that the slurry microbiome predominantly comprised members of the Bacteroidetes, Firmicutes, and Proteobacteria phyla. The rarefaction analysis showed the bacterial species richness varied among the treated samples. Overall, at the OTU level, 2,558 individual genera were classified, 276 genera were found among the three samples, and 1,832 additional genera were identified in the individual samples. A principal component analysis revealed the differences in microbial communities among the CNS, PHR, and SMC pig slurries. Correlation of the bacterial community structure with the Kyoto Encyclopedia of Genes and Genomes (KEGG) predicted pathways showed that the treatments altered the metabolic capabilities of the slurry microbiota. Overall, these results demonstrated that the PHR and S MC treatments significantly reduced the malodor compounds in pig slurry (P < 0.05).

The genome of the intracellular bacterium of the coastal bivalve, Solemya velum: a blueprint for thriving in and out of symbiosis

BACKGROUND

Symbioses between chemoautotrophic bacteria and marine invertebrates are rare examples of living systems that are virtually independent of photosynthetic primary production. These associations have evolved multiple times in marine habitats, such as deep-sea hydrothermal vents and reducing sediments, characterized by steep gradients of oxygen and reduced chemicals. Due to difficulties associated with maintaining these symbioses in the laboratory and culturing the symbiotic bacteria, studies of chemosynthetic symbioses rely heavily on culture independent methods. The symbiosis between the coastal bivalve, Solemya velum, and its intracellular symbiont is a model for chemosynthetic symbioses given its accessibility in intertidal environments and the ability to maintain it under laboratory conditions. To better understand this symbiosis, the genome of the S. velum endosymbiont was sequenced.

RESULTS

Relative to the genomes of obligate symbiotic bacteria, which commonly undergo erosion and reduction, the S. velum symbiont genome was large (2.7 Mb), GC-rich (51%), and contained a large number (78) of mobile genetic elements. Comparative genomics identified sets of genes specific to the chemosynthetic lifestyle and necessary to sustain the symbiosis. In addition, a number of inferred metabolic pathways and cellular processes, including heterotrophy, branched electron transport, and motility, suggested that besides the ability to function as an endosymbiont, the bacterium may have the capacity to live outside the host.

CONCLUSIONS

The physiological dexterity indicated by the genome substantially improves our understanding of the genetic and metabolic capabilities of the S. velum symbiont and the breadth of niches the partners may inhabit during their lifecycle.

Identifying differentially expressed genes and small molecule drugs for prostate cancer by a bioinformatics strategy

PURPOSE

Prostate cancer caused by the abnormal disorderly growth of prostatic acinar cells is the most prevalent cancer of men in western countries. We aimed to screen out differentially expressed genes (DEGs) and explore small molecule drugs for prostate cancer.

MATERIALS AND METHODS

The GSE3824 gene expression profile of prostate cancer was downloaded from Gene Expression Omnibus database which including 21 normal samples and 18 prostate cancer cells. The DEGs were identified by Limma package in R language and gene ontology and pathway enrichment analyses were performed. In addition, potential regulatory microRNAs and the target sites of the transcription factors were screened out based on the molecular signature database. In addition, the DEGs were mapped to the connectivity map database to identify potential small molecule drugs.

RESULTS

A total of 6,588 genes were filtered as DEGs between normal and prostate cancer samples. Examples such as ITGB6, ITGB3, ITGAV and ITGA2 may induce prostate cancer through actions on the focal adhesion pathway. Furthermore, the transcription factor, SP1, and its target genes ARHGAP26 and USF1 were identified. The most significant microRNA, MIR-506, was screened and found to regulate genes including ITGB1 and ITGB3. Additionally, small molecules MS-275, 8-azaguanine and pyrvinium were discovered to have the potential to repair the disordered metabolic pathways, abd furthermore to remedy prostate cancer.

CONCLUSIONS

The results of our analysis bear on the mechanism of prostate cancer and allow screening for small molecular drugs for this cancer. The findings have the potential for future use in the clinic for treatment of prostate cancer.

Mining proteins associated with oral squamous cell carcinoma in complex networks

The purpose of this study was to construct a protein-protein interaction (PPI) network related to oral squamous cell carcinoma (OSCC). Each protein was ranked and those most associated with OSCC were mined within the network. First, OSCC-related genes were retrieved from the Online Mendelian Inheritance in Man (OMIM) database. Then they were mapped to their protein identifiers and a seed set of proteins was built. The seed proteins were expanded using the nearest neighbor expansion method to construct a PPI network through the Online Predicated Human Interaction Database (OPHID). The network was verified to be statistically significant, the score of each protein was evaluated by algorithm, then the OSCC-related proteins were ranked. 38 OSCC related seed proteins were expanded to 750 protein pairs. A protein-protein interaction nerwork was then constructed and the 30 top-ranked proteins listed. The four highest-scoring seed proteins were SMAD4, CTNNB1, HRAS, NOTCH1, and four non-seed proteins P53, EP300, SMAD3, SRC were mined using the nearest neighbor expansion method. The methods shown here may facilitate the discovery of important OSCC proteins and guide medical researchers in further pertinent studies.

Absurdaster, a new genus of basal atelostomate from the Early Cretaceous of Europe and its phylogenetic position

Field work in the Lower Cretaceous of the Dolomites (Italy) has resulted in the recovery of a new genus of 'disasteroid' echinoid, which successively was also discovered in slightly older strata in Northern Hungary. This new genus, Absurdaster, is characterized by its highly modified, disjunct apical disc in which all genital plate except genital plate 2 are reduced or fused. The gonopores (which may be multiple) have shifted and pierce interambulacral plates. Anteriorly ambulacrum III is distinctly sunken and forms a distinct frontal notch, while the posterior end is pointed and features a small sharply defined posterior face bearing the periproct. Two new species are established: Absurdaster puezensis sp. nov. from the Upper Hauterivian to Lower Barremian Puez Formation of Northern Italy is characterized by its rudimentary ambulacral pores in the paired ambulacra, high hexagonal ambulacral plates aborally and multiple gonopores in the most adapical plates of interambulacral columns 1b and 4a. Absurdaster hungaricus sp. nov. from the Lower Hauterivian Bersek Marl Formation of Northern Hungary, in contrast, shows circumflexed ambulacral pores, low ambulacral plates, a single gonopore each in the most adapical plates of interambulacral columns 1b and 4a and a flaring posterior end, with sharp margin and invaginated periproct. In addition to those two species Collyrites meriani Ooster, 1865 from the uppermost Berriasian to basal Barremian of Switzerland is attributed to the new genus. Despite the poor knowledge on this form it seems to be distinguished from the new species by its smaller ambulacral plates and higher interambulacral/ambulacral plate ratio. Phylogenetic analyses based on previous work by Barras (2007) and Saucède et al. (2007) indicate that the new genus is a highly derived stem-group member of the Atelostomata close to the split of holasteroids and spatangoids. A combined analysis based on a subset of the characters employed in these two studies for the first time results in a fully resolved tree for 'disasteroids'. Absurdaster, shows two notable morphological peculiarities: 1) it is one of the first echinoids to develop fascioles and exhibits a yet unknown type of fasciole circling the periproct, termed circumanal fasciole here; 2) it is extraordinary among echinoderms as its extraxial skeleton is reduced to a single plate, the madreporite (genital plate 2), and because its genital pores pierce axial elements rather than extraxial ones.

Genome-scale analysis of DNA methylation in colorectal cancer using Infinium HumanMethylation450 BeadChips

Illumina's Infinium HumanMethylation450 BeadChip arrays were used to examine genome-wide DNA methylation profiles in 22 sample pairs from colorectal cancer (CRC) and adjacent tissues and 19 colon tissue samples from cancer-free donors. We show that the methylation profiles of tumors and healthy tissue samples can be clearly distinguished from one another and that the main source of methylation variability is associated with disease status. We used different statistical approaches to evaluate the methylation data. In general, at the CpG-site level, we found that common CRC-specific methylation patterns consist of at least 15,667 CpG sites that were significantly different from either adjacent healthy tissue or tissue from cancer-free subjects. Of these sites, 10,342 were hypermethylated in CRC, and 5,325 were hypomethylated. Hypermethylated sites were common in the maximum number of sample pairs and were mostly located in CpG islands, where they were significantly enriched for differentially methylated regions known to be cancer-specific. In contrast, hypomethylated sites were mostly located in CpG shores and were generally sample-specific. Despite the considerable variability in methylation data, we selected a panel of 14 highly robust candidates showing methylation marks in genes SND1, ADHFE1, OPLAH, TLX2, C1orf70, ZFP64, NR5A2, and COL4A. This set was successfully cross-validated using methylation data from 209 CRC samples and 38 healthy tissue samples from The Cancer Genome Atlas consortium (AUC = 0.981 [95% CI: 0.9677-0.9939], sensitivity = 100% and specificity = 82%). In summary, this study reports a large number of loci with novel differential methylation statuses, some of which may serve as candidate markers for diagnostic purposes.

Application of genomics, proteomics and metabolomics in drug discovery, development and clinic

Genomics, proteomics and metabolomics are three areas that are routinely applied throughout the drug-development process as well as after a product enters the market. This review discusses all three 'omics, reporting on the key applications, techniques, recent advances and expectations of each. Genomics, mainly through the use of novel and next-generation sequencing techniques, has advanced areas of drug discovery and development through the comparative assessment of normal and diseased-state tissues, transcription and/or expression profiling, side-effect profiling, pharmacogenomics and the identification of biomarkers. Proteomics, through techniques including isotope coded affinity tags, stable isotopic labeling by amino acids in cell culture, isobaric tags for relative and absolute quantification, multidirectional protein identification technology, activity-based probes, protein/peptide arrays, phage displays and two-hybrid systems is utilized in multiple areas through the drug development pipeline including target and lead identification, compound optimization, throughout the clinical trials process and after market analysis. Metabolomics, although the most recent and least developed of the three 'omics considered in this review, provides a significant contribution to drug development through systems biology approaches. Already implemented to some degree in the drug-discovery industry and used in applications spanning target identification through to toxicological analysis, metabolic network understanding is essential in generating future discoveries.

RNA-binding protein PCBP2 modulates glioma growth by regulating FHL3

PCBP2 is a member of the poly(C)-binding protein (PCBP) family, which plays an important role in posttranscriptional and translational regulation by interacting with single-stranded poly(C) motifs in target mRNAs. Several PCBP family members have been reported to be involved in human malignancies. Here, we show that PCBP2 is upregulated in human glioma tissues and cell lines. Knockdown of PCBP2 inhibited glioma growth in vitro and in vivo through inhibition of cell-cycle progression and induction of caspase-3-mediated apoptosis. Thirty-five mRNAs were identified as putative PCBP2 targets/interactors using RIP-ChIP protein-RNA interaction arrays in a human glioma cell line, T98G. Four-and-a-half LIM domain 3 (FHL3) mRNA was downregulated in human gliomas and was identified as a PCBP2 target. Knockdown of PCBP2 enhanced the expression of FHL3 by stabilizing its mRNA. Overexpression of FHL3 attenuated cell growth and induced apoptosis. This study establishes a link between PCBP2 and FHL3 proteins and identifies a new pathway for regulating glioma progression.

Exposure to ionizing radiation induced persistent gene expression changes in mouse mammary gland

Background

Breast tissue is among the most sensitive tissues to the carcinogenic actions of ionizing radiation and epidemiological studies have linked radiation exposure to breast cancer. Currently, molecular understanding of radiation carcinogenesis in mammary gland is hindered due to the scarcity of in vivo long-term follow up data. We undertook this study to delineate radiation-induced persistent alterations in gene expression in mouse mammary glands 2-month after radiation exposure.

Methods

Six to eight week old female C57BL/6J mice were exposed to 2 Gy of whole body γ radiation and mammary glands were surgically removed 2-month after radiation. RNA was isolated and microarray hybridization performed for gene expression analysis. Ingenuity Pathway Analysis (IPA) was used for biological interpretation of microarray data. Real time quantitative PCR was performed on selected genes to confirm the microarray data.

Results

Compared to untreated controls, the mRNA levels of a total of 737 genes were significantly (p<0.05) perturbed above 2-fold of control. More genes (493 genes; 67%) were upregulated than the number of downregulated genes (244 genes; 33%). Functional analysis of the upregulated genes mapped to cell proliferation and cancer related canonical pathways such as ‘ERK/MAPK signaling’, ‘CDK5 signaling’, and ‘14-3-3-mediated signaling’. We also observed upregulation of breast cancer related canonical pathways such as ‘breast cancer regulation by Stathmin1’, and ‘HER-2 signaling in breast cancer’ in IPA. Interestingly, the downregulated genes mapped to fewer canonical pathways involved in cell proliferation. We also observed that a number of genes with tumor suppressor function (GPRC5A, ELF1, NAB2, Sema4D, ACPP, MAP2, RUNX1) persistently remained downregulated in response to radiation exposure. Results from qRT-PCR on five selected differentially expressed genes confirmed microarray data. The PCR data on PPP4c, ELF1, MAPK12, PLCG1, and E2F6 showed similar trend in up and downregulation as has been observed with the microarray.

Conclusions

Exposure to a clinically relevant radiation dose led to long-term activation of mammary gland genes involved in proliferative and metabolic pathways, which are known to have roles in carcinogenesis. When considered along with downregulation of a number of tumor suppressor genes, our study has implications for breast cancer initiation and progression after therapeutic radiation exposure.

Identification of Breast Cancer Prognosis Markers via Integrative Analysis

In breast cancer research, it is of great interest to identify genomic markers associated with prognosis. Multiple gene profiling studies have been conducted for such a purpose. Genomic markers identified from the analysis of single datasets often do not have satisfactory reproducibility. Among the multiple possible reasons, the most important one is the small sample sizes of individual studies. A cost-effective solution is to pool data from multiple comparable studies and conduct integrative analysis. In this study, we collect four breast cancer prognosis studies with gene expression measurements. We describe the relationship between prognosis and gene expressions using the accelerated failure time (AFT) models. We adopt a 2-norm group bridge penalization approach for marker identification. This integrative analysis approach can effectively identify markers with consistent effects across multiple datasets and naturally accommodate the heterogeneity among studies. Statistical and simulation studies demonstrate satisfactory performance of this approach. Breast cancer prognosis markers identified using this approach have sound biological implications and satisfactory prediction performance.

Multiple independent analyses reveal only transcription factors as an enriched functional class associated with microRNAs

Background

Transcription factors (TFs) have long been known to be principally activators of transcription in eukaryotes and prokaryotes. The growing awareness of the ubiquity of microRNAs (miRNAs) as suppressive regulators in eukaryotes, suggests the possibility of a mutual, preferential, self-regulatory connectivity between miRNAs and TFs. Here we investigate the connectivity from TFs and miRNAs to other genes and each other using text mining, TF promoter binding site and 6 different miRNA binding site prediction methods.

Results

In the first approach text mining of PubMed abstracts reveal statistically significant associations between miRNAs and both TFs and signal transduction gene classes. Secondly, prediction of miRNA targets in human and mouse 3’UTRs show enrichment only for TFs but not consistently across prediction methods for signal transduction or other gene classes. Furthermore, a random sample of 986 TarBase entries was scored for experimental evidence by manual inspection of the original papers, and enrichment for TFs was observed to increase with score. Low-scoring TarBase entries, where experimental evidence is anticorrelated miRNA:mRNA expression with predicted miRNA targets, appear not to select for real miRNA targets to any degree. Our manually validated text-mining results also suggests that miRNAs may be activated by more TFs than other classes of genes, as 7% of miRNA:TF co-occurrences in the literature were TFs activating miRNAs. This was confirmed when thirdly, we found enrichment for predicted, conserved TF binding sites in miRNA and TF genes compared to other gene classes.

Conclusions

We see enrichment of connections between miRNAs and TFs using several independent methods, suggestive of a network of mutual activating and suppressive regulation. We have also built regulatory networks (containing 2- and 3-loop motifs) for mouse and human using predicted miRNA and TF binding sites and we have developed a web server to search and display these loops, available for the community at http://rth.dk/resources/tfmirloop.

A historical overview of natural products in drug discovery

Historically, natural products have been used since ancient times and in folklore for the treatment of many diseases and illnesses. Classical natural product chemistry methodologies enabled a vast array of bioactive secondary metabolites from terrestrial and marine sources to be discovered. Many of these natural products have gone on to become current drug candidates. This brief review aims to highlight historically significant bioactive marine and terrestrial natural products, their use in folklore and dereplication techniques to rapidly facilitate their discovery. Furthermore a discussion of how natural product chemistry has resulted in the identification of many drug candidates; the application of advanced hyphenated spectroscopic techniques to aid in their discovery, the future of natural product chemistry and finally adopting metabolomic profiling and dereplication approaches for the comprehensive study of natural product extracts will be discussed.

Single sample expression-anchored mechanisms predict survival in head and neck cancer

Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These “causality challenges” hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate “personal mechanism signatures” of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of “Oncogenic FAIME Features of HNSCC” (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, p<0.001) is more significant than the gene overlap (genes:4%). These Oncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets (n = 35 and 91, F-accuracy = 100% and 97%, empirical p<0.001, area under the receiver operating characteristic curves = 99% and 92%), and stratify recurrence-free survival in patients from two independent studies (p = 0.0018 and p = 0.032, log-rank). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies (e.g. survival time, tumor volume).

Clinical utilization of multi-gene expression signatures that are predictive of therapeutic response has been steadily increasing, however, interpretation of such results remains challenging because multi-gene signatures, generated from analyzing different patient cohorts, tend to be equally predictive but contain minimal overlap. Whereas pathway-level analyses of expression arrays show promise for generating clinically meaningful mechanistic signatures, current approaches do not permit single-patient based analyses that are independent of cross-group calculations. To bridge the gap between deterministic biological mechanisms of single-gene biomarkers and the statistical predictive power of multi-gene signatures that are disconnected from mechanisms, we developed FAIME, a novel method that transforms microarray gene expression data into individualized patient profiles of molecular mechanisms. We have validated its capability for predicting clinical outcomes, including cancer patient samples derived from six different clinical trial cohorts of head and neck cancers. This method provides opportunities to harness an untapped resource for personal genomics: clinical evaluation and testing of individually interpretable mechanistic profiles derived from gene expression arrays.

Fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps

Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways.We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our "pathways group lasso with adaptive weights" (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets.In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small.

Integrative analysis of multiple cancer prognosis studies with gene expression measurements

Although in cancer research microarray gene profiling studies have been successful in identifying genetic variants predisposing to the development and progression of cancer, the identified markers from analysis of single datasets often suffer low reproducibility. Among multiple possible causes, the most important one is the small sample size hence the lack of power of single studies. Integrative analysis jointly considers multiple heterogeneous studies, has a significantly larger sample size, and can improve reproducibility. In this article, we focus on cancer prognosis studies, where the response variables are progression-free, overall, or other types of survival. A group minimax concave penalty (GMCP) penalized integrative analysis approach is proposed for analyzing multiple heterogeneous cancer prognosis studies with microarray gene expression measurements. An efficient group coordinate descent algorithm is developed. The GMCP can automatically accommodate the heterogeneity across multiple datasets, and the identified markers have consistent effects across multiple studies. Simulation studies show that the GMCP provides significantly improved selection results as compared with the existing meta-analysis approaches, intensity approaches, and group Lasso penalized integrative analysis. We apply the GMCP to four microarray studies and identify genes associated with the prognosis of breast cancer.

Simple Neurite Tracer: open source software for reconstruction, visualization and analysis of neuronal processes

MOTIVATION

Advances in techniques to sparsely label neurons unlock the potential to reconstruct connectivity from 3D image stacks acquired by light microscopy. We present an application for semi-automated tracing of neurons to quickly annotate noisy datasets and construct complex neuronal topologies, which we call the Simple Neurite Tracer.

AVAILABILITY

Simple Neurite Tracer is open source software, licensed under the GNU General Public Licence (GPL) and based on the public domain image processing software ImageJ. The software and further documentation are available via http://fiji.sc/Simple_Neurite_Tracer as part of the package Fiji, and can be used on Windows, Mac OS and Linux. Documentation and introductory screencasts are available at the same URL.

CONTACT

longair@ini.phys.ethz.ch; longair@ini.phys.ethz.ch.

Identification of predictive pathways for non-hodgkin lymphoma prognosis

Despite decades of intensive research, NHL (non-Hodgkin lymphoma) still remains poorly understood and is largely incurable. Recent molecular studies suggest that genomic variants measured with SNPs (single nucleotide polymorphisms) in genes may have additional predictive power for NHL prognosis beyond clinical risk factors. We analyzed a genetic association study. The prognostic cohort consisted of 346 patients, among whom 138 had DLBCL (diffuse large B-cell lymphoma) and 101 had FL ( follicular lymphoma). For DLBCL, we analyzed 1229 SNPs which represented 122 KEGG pathways. For FL, we analyzed 1228 SNPs which represented 122 KEGG pathways. Unlike in existing studies, we targeted at identifying pathways with significant additional predictive power beyond clinical factors. In addition, we accounted for the joint effects of multiple SNPs within pathways, whereas some existing studies drew pathway-level conclusions based on separate analysis of individual SNPs. For DLBCL, we identified four pathways, which, combined with the clinical factors, had medians of the prediction logrank statistics as 2.535, 2.220, 2.094, 2.453, and 2.512, respectively. As a comparison, the clinical factors had a median of the prediction logrank statistics around 0.552. For FL, we identified two pathways, which, combined with the clinical factors, had medians of the prediction logrank statistics as 4.320 and 3.532, respectively. As a comparison, the clinical factors had a median of the prediction logrank statistics around 1.212. For NHL overall, we identified three pathways, which, combined with the clinical factors, had medians of the prediction logrank statistics as 5.722, 5.314, and 5.441, respective. As a comparison, the clinical factors had a median of the prediction logrank statistics around 4.411. The identified pathways have sound biological bases. In addition, they are different from those identified using existing approaches. They may provide further insights into the biological mechanisms underlying the prognosis of NHL.

Supramolecular self-assembled chaos: polyphenolic lignin's barrier to cost-effective lignocellulosic biofuels

Phenylpropanoid metabolism yields a mixture of monolignols that undergo chaotic, non-enzymatic reactions such as free radical polymerization and spontaneous self-assembly in order to form the polyphenolic lignin which is a barrier to cost-effective lignocellulosic biofuels. Post-synthesis lignin integration into the plant cell wall is unclear, including how the hydrophobic lignin incorporates into the wall in an initially hydrophilic milieu. Self-assembly, self-organization and aggregation give rise to a complex, 3D network of lignin that displays randomly branched topology and fractal properties. Attempts at isolating lignin, analogous to archaeology, are instantly destructive and non-representative of in planta. Lack of plant ligninases or enzymes that hydrolyze specific bonds in lignin-carbohydrate complexes (LCCs) also frustrate a better grasp of lignin. Supramolecular self-assembly, nano-mechanical properties of lignin-lignin, lignin-polysaccharide interactions and association-dissociation kinetics affect biomass deconstruction and thereby cost-effective biofuels production.

A genome-wide association scan on estrogen receptor-negative breast cancer

Introduction

Breast cancer is a heterogeneous disease and may be characterized on the basis of whether estrogen receptors (ER) are expressed in the tumour cells. ER status of breast cancer is important clinically, and is used both as a prognostic indicator and treatment predictor. In this study, we focused on identifying genetic markers associated with ER-negative breast cancer risk.

Methods

We conducted a genome-wide association analysis of 285,984 single nucleotide polymorphisms (SNPs) genotyped in 617 ER-negative breast cancer cases and 4,583 controls. We also conducted a genome-wide pathway analysis on the discovery dataset using permutation-based tests on pre-defined pathways. The extent of shared polygenic variation between ER-negative and ER-positive breast cancers was assessed by relating risk scores, derived using ER-positive breast cancer samples, to disease state in independent, ER-negative breast cancer cases.

Results

Association with ER-negative breast cancer was not validated for any of the five most strongly associated SNPs followed up in independent studies (1,011 ER-negative breast cancer cases, 7,604 controls). However, an excess of small P-values for SNPs with known regulatory functions in cancer-related pathways was found (global P = 0.052). We found no evidence to suggest that ER-negative breast cancer shares a polygenic basis to disease with ER-positive breast cancer.

Conclusions

ER-negative breast cancer is a distinct breast cancer subtype that merits independent analyses. Given the clinical importance of this phenotype and the likelihood that genetic effect sizes are small, greater sample sizes and further studies are required to understand the etiology of ER-negative breast cancers.

A combined analysis of genome-wide association studies in breast cancer

In an attempt to identify common disease susceptibility alleles for breast cancer, we performed a combined analysis of three genome-wide association studies (GWAS), involving 2,702 women of European ancestry with invasive breast cancer and 5,726 controls. Tests for association were performed for 285,984 SNPs. Evidence for association with SNPs in genes in specific pathways was assessed using a permutation-based approach. We confirmed associations with loci reported by previous GWAS on 1p11.2, 2q35, 3p, 5p12, 8q24, 10q23.13, 14q24.1 and 16q. Six SNPs with the strongest signals of association with breast cancer, and which have not been reported previously, were typed in two further studies; however, none of the associations could be confirmed. Suggestive evidence for an excess of associations was found for genes involved in the regulation of actin cytoskeleton, glycan degradation, alpha-linolenic acid metabolism, circadian rhythm, hematopoietic cell lineage and drug metabolism. Androgen and oestrogen metabolism, a pathway previously found to be associated with the development of postmenopausal breast cancer, was marginally significant (P = 0.051 [unadjusted]). These results suggest that further analysis of SNPs in these pathways may identify associations that would be difficult to detect through agnostic single SNP analyses. More effort focused in these aspects of oncology can potentially open up promising avenues for the understanding of breast cancer and its prevention.