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Jonathan D Wren

Publications and source records attributed to Jonathan D Wren.

At least 19 recordsLinked to original sources
PubMed2022

Lipid hydroperoxides and oxylipins are mediators of denervation induced muscle atrophy.

Loss of innervation is a key driver of age associated muscle atrophy and weakness (sarcopenia). Our laboratory has previously shown that denervation induced atrophy is associated with the generation of mitochondrial hydroperoxides and lipid mediators produced downstream of cPLA2 and 12/15 lipoxygenase (12/15-LOX). To define the pathological impact of lipid hydroperoxides generated in denervation-induced atrophy in vivo, we treated mice with liproxstatin-1, a lipid hydroperoxide scavenger. We treated adult male mice with 5 mg/kg liproxstain-1 or vehicle one day prior to sciatic nerve transection and daily for 7 days post-denervation before tissue analysis. Liproxstatin-1 treatment protected gastrocnemius mass and fiber cross sectional area (∼40% less atrophy post-denervation in treated versus untreated mice). Mitochondrial hydroperoxide generation was reduced 80% in vitro and by over 65% in vivo by liproxstatin-1 treatment in denervated permeabilized muscle fibers and decreased the content of 4-HNE by ∼25% post-denervation. Lipidomic analysis revealed detectable levels of 25 oxylipins in denervated gastrocnemius muscle and significantly increased levels for eight oxylipins that are generated by metabolism of fatty acids through 12/15-LOX. Liproxstatin-1 treatment reduced the level of three of the eight denervation-induced oxylipins, specifically 15-HEPE, 13-HOTrE and 17-HDOHE. Denervation elevated protein degradation rates in muscle and treatment with liproxstatin-1 reduced rates of protein breakdown in denervated muscle. In contrast, protein synthesis rates were unchanged by denervation. Targeted proteomics revealed a number of proteins with altered expression after denervation but no effect of liproxstain-1. Transcriptomic analysis revealed 203 differentially expressed genes in denervated muscle from vehicle or liproxstatin-1 treated mice, including ER stress, nitric oxide signaling, Gαi signaling, glucocorticoid receptor signaling, and other pathways. Overall, these data suggest lipid hydroperoxides and oxylipins are key drivers of increased protein breakdown and muscle loss associated with denervation induced atrophy and a potential target for sarcopenia intervention.

Male↗
PubMed2005

Markov model recognition and classification of DNA/protein sequences within large text databases.

MOTIVATION: Short sequence patterns frequently define regions of biological interest (binding sites, immune epitopes, primers, etc.), yet a large fraction of this information exists only within the scientific literature and is thus difficult to locate via conventional means (e.g. keyword queries or manual searches). We describe herein a system to accurately identify and classify sequence patterns from within large corpora using an n-gram Markov model (MM). RESULTS: As expected, on test sets we found that identification of sequences with limited alphabets and/or regular structures such as nucleic acids (non-ambiguous) and peptide abbreviations (3-letter) was highly accurate, whereas classification of symbolic (1-letter) peptide strings with more complex alphabets was more problematic. The MM was used to analyze two very large, sequence-containing corpora: over 7.75 million Medline abstracts and 9000 full-text articles from Journal of Virology. Performance was benchmarked by comparing the results with Journal of Virology entries in two existing manually curated databases: VirOligo and the HLA Ligand Database. Performance estimates were 98 +/- 2% precision/84% recall for primer identification and classification and 67 +/- 6% precision/85% recall for peptide epitopes. We also find a dramatic difference between the amounts of sequence-related data reported in abstracts versus full text. Our results suggest that automated extraction and classification of sequence elements is a promising, low-cost means of sequence database curation and annotation. AVAILABILITY: MM routine and datasets are available upon request.

Abstracting and Indexing↗
PubMed2005

Automating genomic data mining via a sequence-based matrix format and associative rule set.

There is an enormous amount of information encoded in each genome--enough to create living, responsive and adaptive organisms. Raw sequence data alone is not enough to understand function, mechanisms or interactions. Changes in a single base pair can lead to disease, such as sickle-cell anemia, while some large megabase deletions have no apparent phenotypic effect. Genomic features are varied in their data types and annotation of these features is spread across multiple databases. Herein, we develop a method to automate exploration of genomes by iteratively exploring sequence data for correlations and building upon them. First, to integrate and compare different annotation sources, a sequence matrix (SM) is developed to contain position-dependant information. Second, a classification tree is developed for matrix row types, specifying how each data type is to be treated with respect to other data types for analysis purposes. Third, correlative analyses are developed to analyze features of each matrix row in terms of the other rows, guided by the classification tree as to which analyses are appropriate. A prototype was developed and successful in detecting coinciding genomic features among genes, exons, repetitive elements and CpG islands.

Base Sequence↗
PubMed2005

A microarray study of MPP+-treated PC12 Cells: Mechanisms of toxicity (MOT) analysis using bioinformatics tools.

BACKGROUND: This paper describes a microarray study including data quality control, data analysis and the analysis of the mechanism of toxicity (MOT) induced by 1-methyl-4-phenylpyridinium (MPP+) in a rat adrenal pheochromocytoma cell line (PC12 cells) using bioinformatics tools. MPP+ depletes dopamine content and elicits cell death in PC12 cells. However, the mechanism of MPP+-induced neurotoxicity is still unclear. RESULTS: In this study, Agilent rat oligo 22K microarrays were used to examine alterations in gene expression of PC12 cells after 500 muM MPP+ treatment. Relative gene expression of control and treated cells represented by spot intensities on the array chips was analyzed using bioinformatics tools. Raw data from each array were input into the NCTR ArrayTrack database, and normalized using a Lowess normalization method. Data quality was monitored in ArrayTrack. The means of the averaged log ratio of the paired samples were used to identify the fold changes of gene expression in PC12 cells after MPP+ treatment. Our data showed that 106 genes and ESTs (Expressed Sequence Tags) were changed 2-fold and above with MPP+ treatment; among these, 75 genes had gene symbols and 59 genes had known functions according to the Agilent gene Refguide and ArrayTrack-linked gene library. The mechanism of MPP+-induced toxicity in PC12 cells was analyzed based on their genes functions, biological process, pathways and previous published literatures. CONCLUSION: Multiple pathways were suggested to be involved in the mechanism of MPP+-induced toxicity, including oxidative stress, DNA and protein damage, cell cycling arrest, and apoptosis.

1-Methyl-4-phenylpyridinium↗
PubMed2005

Data-mining analysis suggests an epigenetic pathogenesis for type 2 diabetes.

The etiological origin of type 2 diabetes mellitus (T2DM) has long been controversial. The body of literature related to T2DM is vast and varied in focus, making a broad epidemiological perspective difficult, if not impossible. A data-mining approach was used to analyze all electronically available scientific literature, over 12 million Medline records, for "objects" such as genes, diseases, phenotypes, and chemical compounds linked to other objects within the T2DM literature but were not themselves within the T2DM literature. The goal of this analysis was to conduct a comprehensive survey to identify novel factors implicated in the pathology of T2DM by statistically evaluating mutually shared associations. Surprisingly, epigenetic factors were among the highest statistical scores in this analysis, strongly implicating epigenetic changes within the body as causal factors in the pathogenesis of T2DM. Further analysis implicates adipocytes as the potential tissue of origin, and cytokines or cytokine-like genes as the dysregulated factor(s) responsible for the T2DM phenotype. The analysis provides a wealth of literature supporting this hypothesis, which-if true-represents an important paradigm shift for researchers studying the pathogenesis of T2DM.

Journal Article↗
PubMed2005

Open access and openly accessible: a study of scientific publications shared via the internet.

OBJECTIVES: To determine how often reprints of scientific publications are shared online, whether journal readership level is a predictor, how the amount of file sharing changes with the age of the article, and to what degree open access publications are shared on non-journal websites. DESIGN: The internet was searched using an application programming interface to Google, a popular and freely available search engine. MAIN OUTCOME MEASURES: The proportion of reprints of journal articles published between 1994 and 2004 from within 13 subscription based and four open access journals that could be located online at non-journal websites. RESULTS: The probability that an article could be found online at a non-journal website correlated with the journal impact factor and the time since initial publication. Papers from higher impact journals and more recent articles were more likely to be located. On average, for the high impact journal articles published in 2003, over a third could be located at non-journal websites. Similar trends were observed for the delayed or full open access publications. CONCLUSIONS: Decentralised sharing of scientific reprints through the internet creates a degree of de facto open access that, though highly incomplete in its coverage, is none the less biased towards publications of higher popular demand.

Bibliometrics↗
PubMed2005

Biomedical term mapping databases.

Longer words and phrases are frequently mapped onto a shorter form such as abbreviations or acronyms for efficiency of communication. These abbreviations are pervasive in all aspects of biology and medicine and as the amount of biomedical literature grows, so does the number of abbreviations and the average number of definitions per abbreviation. Even more confusing, different authors will often abbreviate the same word/phrase differently. This ambiguity impedes our ability to retrieve information, integrate databases and mine textual databases for content. Efforts to standardize nomenclature, especially those doing so retrospectively, need to be aware of different abbreviatory mappings and spelling variations. To address this problem, there have been several efforts to develop computer algorithms to identify the mapping of terms between short and long form within a large body of literature. To date, four such algorithms have been applied to create online databases that comprehensively map biomedical terms and abbreviations within MEDLINE: ARGH (http://lethargy.swmed.edu/ARGH/argh.asp), the Stanford Biomedical Abbreviation Server (http://bionlp.stanford.edu/abbreviation/), AcroMed (http://medstract.med.tufts.edu/acro1.1/index.htm) and SaRAD (http://www.hpl.hp.com/research/idl/projects/abbrev.html). In addition to serving as useful computational tools, these databases serve as valuable references that help biologists keep up with an ever-expanding vocabulary of terms.

Abbreviations as Topic↗
PubMed2005

Differential activation profiles of multiple transcription factors during dendritic cell maturation.

Immature dendritic cells (DC) at the environmental interfaces, such as the skin, constantly survey the tissue for the emergence of microbial products and pro-inflammatory mediators. Upon recognition of such "danger" signals, they undergo dynamic reprogramming of gene expression and functions, the process known as DC maturation, which plays critical roles in both innate and adaptive immune responses. Although DC have been shown to discriminate different maturation stimuli by expressing stimulus-specific signature genes and unique phenotypic and functional properties, underlying mechanisms for this extraordinary plasticity remain relatively unclear. We hypothesized that DC might activate unique sets of transcription factors (TF) upon sensing different stimuli. To test this hypothesis, we transduced a mouse epidermal-derived DC line XS106 to express the luciferase reporter gene under the control of each of 15 different cis-enhancer elements. The resulting DC panels were then exposed to 14 different microbial, endogenous, environmental, and pharmacological agents that produced unique maturational changes. This approach allowed systematic determination of TF activation profiles in DC. Our results revealed striking diversity, with different classes of stimuli triggering preferential activation of distinct sets of TF. We propose that differential TF usage represents a previously unrecognized mechanism regulating the direction of DC maturation.

Animals↗
PubMed2004

Extending the mutual information measure to rank inferred literature relationships.

BACKGROUND: Within the peer-reviewed literature, associations between two things are not always recognized until commonalities between them become apparent. These commonalities can provide justification for the inference of a new relationship where none was previously known, and are the basis of most observation-based hypothesis formation. It has been shown that the crux of the problem is not finding inferable associations, which are extraordinarily abundant given the scale-free networks that arise from literature-based associations, but determining which ones are informative. The Mutual Information Measure (MIM) is a well-established method to measure how informative an association is, but is limited to direct (i.e. observable) associations. RESULTS: Herein, we attempt to extend the calculation of mutual information to indirect (i.e. inferable) associations by using the MIM of shared associations. Objects of general research interest (e.g. genes, diseases, phenotypes, drugs, ontology categories) found within MEDLINE are used to create a network of associations for evaluation. CONCLUSIONS: Mutual information calculations can be effectively extended into implied relationships and a significance cutoff estimated from analysis of random word networks. Of the models tested, the shared minimum MIM (MMIM) model is found to correlate best with the observed strength and frequency of known associations. Using three test cases, the MMIM method tends to rank more specific relationships higher than counting the number of shared relationships within a network.

Humans↗
PubMed2004

Shared relationship analysis: ranking set cohesion and commonalities within a literature-derived relationship network.

MOTIVATION: There is a general scientific need to be able to identify and evaluate what any given set of 'objects' (e.g. genes, phenotypes, chemicals, diseases) has in common. Whether it is to classify, expand upon or identify commonalities and functional groupings, informational needs can be diverse and the best source to identify relationships among a potentially heterogeneous set of objects is the scientific literature. RESULTS: We first establish a network of related objects by their co-occurrence within MEDLINE records. A set of objects within this network can then be queried to identify shared relationships, and a method is presented to score their statistical relevance by comparing observed frequencies with what would be expected in a random network model. Using Gene Ontology (GO) categories, we demonstrate that this method enables a quantitative ranking of the 'cohesiveness' of a set of objects and, importantly, allows other objects related to this set to be identified and evaluated for their 'cohesion' to it. Supplemental information: A list of ranked genes related to each GO category analyzed can be found at http://innovation.swmed.edu/IRIDESCENT/GO_relationships.htm

Abstracting and Indexing↗
PubMed2004

Knowledge discovery by automated identification and ranking of implicit relationships.

MOTIVATION: New relationships are often implicit from existing information, but the amount and growth of published literature limits the scope of analysis an individual can accomplish. Our goal was to develop and test a computational method to identify relationships within scientific reports, such that large sets of relationships between unrelated items could be sought out and statistically ranked for their potential relevance as a set. RESULTS: We first construct a network of tentative relationships between 'objects' of biomedical research interest (e.g. genes, diseases, phenotypes, chemicals) by identifying their co-occurrences within all electronically available MEDLINE records. Relationships shared by two unrelated objects are then ranked against a random network model to estimate the statistical significance of any given grouping. When compared against known relationships, we find that this ranking correlates with both the probability and frequency of object co-occurrence, demonstrating the method is well suited to discover novel relationships based upon existing shared relationships. To test this, we identified compounds whose shared relationships predicted they might affect the development and/or progression of cardiac hypertrophy. When laboratory tests were performed in a rodent model, chlorpromazine was found to reduce the progression of cardiac hypertrophy.

Abstracting and Indexing↗
PubMed2004

404 not found: the stability and persistence of URLs published in MEDLINE.

MOTIVATION: The advent of the World Wide Web has enabled unprecedented supplementation of traditional journal publications, allowing access to resources, such as video, sound, software, databases, datasets too large to publish, and even supplementary information and discussion. However, unlike traditional publications, continued availability of these online resources is not guaranteed. An automated survey was conducted to quantify the growth in Uniform Resource Locators (URLs) published to date in MEDLINE abstracts, their current availability and distribution by journal. RESULTS: Of 1630 unique URLs identified, formatting and/or spelling errors were detected within 201 (12%) of them as published. After corrections were made, a survey revealed that approximately 63% of these URLs were consistently available, and another 19% were available intermittently. The rate of failure was far worse for anonymous login to FTP sites, with only 12 of 33 sites (36%) responding. This survey also shows that journals vary disproportionately in the number of web citations published, suggesting policy implementation among a few could have a profound impact overall. Out of the 306 journals with a URL published in an abstract, Bioinformatics published the most (12% of total). AVAILABILITY: URL database and program available by request.

Abstracting and Indexing↗
PubMed2004

Simulated annealing of microarray data reduces noise and enables cross-experimental comparisons.

Microarrays are a powerful tool for assessing the genome-wide induction of a transcriptional response to internal or external stimuli, but are not considered quantitatively rigorous (i.e., the signal intensity of hybridized probe is normally used to quantify relative transcript abundance). Thus, it is difficult, if not impossible, to accurately compare separate microarray experiments without a reference standard. However, even among replicated microarray experiments, each gene varies significantly in the amount of signal detected, suggesting no single gene would be appropriate as a standard. We propose and test a method to "align" experimental transcription profiles to a set of reference experiments using simulated annealing (SA), essentially using the relative positions of all genes as a reference standard. SA attempts to find a globally optimal adjustment factor for the relative expression level of each experimental gene expression signal, given a previously observed range of gene expression measurements. By defining a relative dynamic range of gene expression under control conditions for all genes, we can more accurately compare transcription profiles between separate experiments and, potentially, between species--enabling comparative transcriptomics. Testing SA on a published dataset, we find that it significantly reduces interexperimental variation, suggesting it holds promise to accomplish this goal.

Oligonucleotide Array Sequence Analysis↗
PubMed2002

SIGNAL-Sequence Information and GeNomic AnaLysis.

An integrated software package has been developed to provide convenient graphical and textual analysis of a variety of genomic sequence features, free of charge to the biomedical research community. This package, called sequence information and genomic analysis, is available as either a stand-alone or a web-based version to enable greater versatility in access and utilization. The package can be accessed or downloaded at the following URL: http://innovation.swmed.edu/signal.htm.

Algorithms↗
PubMed2002

ARROGANT: an application to manipulate large gene collections.

ARROGANT (ARRay OrGANizing Tool) is a software tool developed to facilitate the identification, annotation and comparison of large collections of genes or clones. The objective is to enable users to compile gene/clone collections from different databases, allowing them to design experiments and analyze the collections as well as associated experimental data efficiently. ARROGANT can relate different sequence identifiers to their common reference sequence using the UniGene database, allowing for the comparison of data from two different microarray experiments. ARROGANT has been successfully used to analyze microarray expression data for colon cancer, to compile genes potentially related to cardiac diseases for subsequent resequencing (to identify single nucleotide polymorphisms, SNPs), to design a new comprehensive human cDNA microarray for cancer, to combine and compare expression data generated by different microarrays and to provide annotation for genes on custom and Affymetrix chips.

Base Sequence↗