Bounded Search for de Novo Identification of Degenerate Cis-Regulatory Elements

The identification of statistically overrepresented sequences in the upstream regions of coregulated genes should theoretically permit the identification of potential cis-regulatory elements. However, in practice many cis-regulatory elements are highly degenerate, precluding the use of an exhaustive word-counting strategy for their identification. While numerous methods exist for inferring base distributions using a position weight matrix, recent studies suggest that the independence assumptions inherent in the model, as well as the inability to reach a global optimum, limit this approach

A Novel Ensemble Learning Method for de Novo Computational Identification of DNA Binding Sites

Despite the diversity of motif representations and search algorithms, the de novo computational identification of transcription factor binding sites remains constrained by the limited accuracy of existing algorithms and the need for user-specified input parameters that describe the motif being sought.ResultsWe present a novel ensemble learning method, SCOPE, that is based on the assumption that transcription factor binding sites belong to one of three broad classes of motifs: non-degenerate, degenerate and gapped motifs. SCOPE employs a unified scoring metric to combine the results from three motif finding algorithms each aimed at the discovery of one of these classes of motifs. We found that SCOPE\u27s performance on 78 experimentally characterized regulons from four species was a substantial and statistically significant improvement over that of its component algorithms. SCOPE outperformed a broad range of existing motif discovery algorithms on the same dataset by a statistically significant margin

Quantum dots to monitor RNAi delivery and improve gene silencing

A critical issue in using RNA interference for identifying genotype/phenotype correlations is the uniformity of gene silencing within a cell population. Variations in transfection efficiency, delivery-induced cytotoxicity and ‘off target’ effects at high siRNA concentrations can confound the interpretation of functional studies. To address this problem, we have developed a novel method of monitoring siRNA delivery that combines unmodified siRNA with seminconductor quantum dots (QDs) as multi color biological probes. We co-transfected siRNA with QDs using standard transfection techniques, thereby leveraging the photostable fluorescent nanoparticles to track delivery of nucleic acid, sort cells by degree of transfection and purify homogenously-silenced subpopulations. Compared to alternative RNAi tracking methods (co-delivery of reporter plasmids and end-labeling the siRNA), QDs exhibit superior photostability and tunable optical properties for an extensive selection of non-overlapping colors. Thus this simple, modular system can be extended toward multiplexed gene knockdown studies, as demonstrated in a two color proof-of-principle study with two biological targets. When the method was applied to investigate the functional role of T-cadherin (T-cad) in cell–cell communication, a subpopulation of highly silenced cells obtained by QD labeling was required to observe significant downstream effects of gene knockdown

Label-free segmentation of co-cultured cells on a nanotopographical gradient

The function and fate of cells is influenced by many different factors, one of which is surface topography of the support culture substrate. Systematic studies of nanotopography and cell response have typically been limited to single cell types and a small set of topographical variations. Here, we show a radical expansion of experimental throughput using automated detection, measurement, and classification of co-cultured cells on a nanopillar array where feature height changes continuously from planar to 250 nm over 9 mm. Individual cells are identified and characterized by more than 200 descriptors, which are used to construct a set of rules for label-free segmentation into individual cell types. Using this approach we can achieve label-free segmentation with 84% confidence across large image data sets and suggest optimized surface parameters for nanostructuring of implant devices such as vascular stents

β[beta]-Catenin and FGFR2 regulate postnatal rosette-based adrenocortical morphogenesis

Rosettes are widely used in epithelial morphogenesis during embryonic development and organogenesis. However, their role in postnatal development and adult tissue maintenance remains largely unknown. Here, we show zona glomerulosa cells in the adult adrenal cortex organize into rosettes through adherens junction-mediated constriction, and that rosette formation underlies the maturation of adrenal glomerular structure postnatally. Using genetic mouse models, we show loss of beta-catenin results in disrupted adherens junctions, reduced rosette number, and dysmorphic glomeruli, whereas beta-catenin stabilization leads to increased adherens junction abundance, more rosettes, and glomerular expansion. Furthermore, we uncover numerous known regulators of epithelial morphogenesis enriched in beta-catenin-stabilized adrenals. Among these genes, we show Fgfr2 is required for adrenal rosette formation by regulating adherens junction abundance and aggregation. Together, our data provide an example of rosette-mediated postnatal tissue morphogenesis and a framework for studying the role of rosettes in adult zona glomerulosa tissue maintenance and function

Intronic Non-CG DNA hydroxymethylation and alternative mRNA splicing in honey bees

Abstract Background Previous whole-genome shotgun bisulfite sequencing experiments showed that DNA cytosine methylation in the honey bee (Apis mellifera) is almost exclusively at CG dinucleotides in exons. However, the most commonly used method, bisulfite sequencing, cannot distinguish 5-methylcytosine from 5-hydroxymethylcytosine, an oxidized form of 5-methylcytosine that is catalyzed by the TET family of dioxygenases. Furthermore, some analysis software programs under-represent non-CG DNA methylation and hydryoxymethylation for a variety of reasons. Therefore, we used an unbiased analysis of bisulfite sequencing data combined with molecular and bioinformatics approaches to distinguish 5-methylcytosine from 5-hydroxymethylcytosine. By doing this, we have performed the first whole genome analyses of DNA modifications at non-CG sites in honey bees and correlated the effects of these DNA modifications on gene expression and alternative mRNA splicing. Results We confirmed, using unbiased analyses of whole-genome shotgun bisulfite sequencing (BS-seq) data, with both new data and published data, the previous finding that CG DNA methylation is enriched in exons in honey bees. However, we also found evidence that cytosine methylation and hydroxymethylation at non-CG sites is enriched in introns. Using antibodies against 5-hydroxmethylcytosine, we confirmed that DNA hydroxymethylation at non-CG sites is enriched in introns. Additionally, using a new technique, Pvu-seq (which employs the enzyme PvuRts1l to digest DNA at 5-hydroxymethylcytosine sites followed by next-generation DNA sequencing), we further confirmed that hydroxymethylation is enriched in introns at non-CG sites. Conclusions Cytosine hydroxymethylation at non-CG sites might have more functional significance than previously appreciated, and in honey bees these modifications might be related to the regulation of alternative mRNA splicing by defining the locations of the introns

TBI lesion segmentation in head CT: impact of preprocessing and data augmentation

Automatic segmentation of lesions in head CT provides keyinformation for patient management, prognosis and disease monitoring.Despite its clinical importance, method development has mostly focusedon multi-parametric MRI. Analysis of the brain in CT is challengingdue to limited soft tissue contrast and its mono-modal nature. We studythe under-explored problem of fine-grained CT segmentation of multiplelesion types (core, blood, oedema) in traumatic brain injury (TBI). Weobserve that preprocessing and data augmentation choices greatly impactthe segmentation accuracy of a neural network, yet these factors arerarely thoroughly assessed in prior work. We design an empirical studythat extensively evaluates the impact of different data preprocessing andaugmentation methods. We show that these choices can have an impactof up to 18% DSC. We conclude that resampling to isotropic resolutionyields improved performance, skull-stripping can be replaced by using theright intensity window, and affine-to-atlas registration is not necessaryif we use sufficient spatial augmentation. Since both skull-stripping andaffine-to-atlas registration are susceptible to failure, we recommend theiralternatives to be used in practice. We believe this is the first work toreport results for fine-grained multi-class segmentation of TBI in CT. Ourfindings may inform further research in this under-explored yet clinicallyimportant task of automatic head CT lesion segmentation

BSQA: integrated text mining using entity relation semantics extracted from biological literature of insects

Text mining is one promising way of extracting information automatically from the vast biological literature. To maximize its potential, the knowledge encoded in the text should be translated to some semantic representation such as entities and relations, which could be analyzed by machines. But large-scale practical systems for this purpose are rare. We present BeeSpace question/answering (BSQA) system that performs integrated text mining for insect biology, covering diverse aspects from molecular interactions of genes to insect behavior. BSQA recognizes a number of entities and relations in Medline documents about the model insect, Drosophila melanogaster. For any text query, BSQA exploits entity annotation of retrieved documents to identify important concepts in different categories. By utilizing the extracted relations, BSQA is also able to answer many biologically motivated questions, from simple ones such as, which anatomical part is a gene expressed in, to more complex ones involving multiple types of relations. BSQA is freely available at http://www.beespace.uiuc.edu/QuestionAnswer

A novel human skin chamber model to study wound infection ex vivo

Wound infections with multi-drug resistant bacteria increase morbidity and mortality and have considerable socioeconomic impact. They can lead to impaired wound healing, resulting in rising treatment costs. The aim of this study was to investigate an ex vivo human wound infection model. Human full-thickness skin from the operating room (OR) was placed into the Bo-Drum® and cultivated for 7 days in an air–liquid interphase. On day 8, the skin was inoculated with either (1) Pseudomonas aeruginosa, (2) Staphylococcus aureus (105 CFU, n = 3) or (3) carrier control. 1, 3 and 7 days after inoculation colony forming units in the tissue/media were determined and cytokine expression was quantified. A reliable and reproducible wound infection could be established for 7 days. At this timepoint, 1.8 × 108 CFU/g tissue of P. aeruginosa and 2 × 107 CFU/g tissue of S. aureus were detected. Immunohistochemical analysis demonstrated bacterial infection and epidermolysis in infected skin. RT-PCR analysis exhibited a significant induction of proinflammatory cytokines after infection. The BO-drum® is a robust, easy-to-use, sterilizable and reusable ex vivo full-skin culture system. For investigation of wound infection, treatment and healing, the BO-drum® presents a convenient model and may help to standardize wound research