MAPPER: a search engine for the computational identification of putative transcription factor binding sites in multiple genomes

BACKGROUND: Cis-regulatory modules are combinations of regulatory elements occurring in close proximity to each other that control the spatial and temporal expression of genes. The ability to identify them in a genome-wide manner depends on the availability of accurate models and of search methods able to detect putative regulatory elements with enhanced sensitivity and specificity. RESULTS: We describe the implementation of a search method for putative transcription factor binding sites (TFBSs) based on hidden Markov models built from alignments of known sites. We built 1,079 models of TFBSs using experimentally determined sequence alignments of sites provided by the TRANSFAC and JASPAR databases and used them to scan sequences of the human, mouse, fly, worm and yeast genomes. In several cases tested the method identified correctly experimentally characterized sites, with better specificity and sensitivity than other similar computational methods. Moreover, a large-scale comparison using synthetic data showed that in the majority of cases our method performed significantly better than a nucleotide weight matrix-based method. CONCLUSION: The search engine, available at , allows the identification, visualization and selection of putative TFBSs occurring in the promoter or other regions of a gene from the human, mouse, fly, worm and yeast genomes. In addition it allows the user to upload a sequence to query and to build a model by supplying a multiple sequence alignment of binding sites for a transcription factor of interest. Due to its extensive database of models, powerful search engine and flexible interface, MAPPER represents an effective resource for the large-scale computational analysis of transcriptional regulation

Identification of autoimmune gene signatures in autism

The role of the immune system in neuropsychiatric diseases, including autism spectrum disorder (ASD), has long been hypothesized. This hypothesis has mainly been supported by family cohort studies and the immunological abnormalities found in ASD patients, but had limited findings in genetic association testing. Two cross-disorder genetic association tests were performed on the genome-wide data sets of ASD and six autoimmune disorders. In the polygenic score test, we examined whether ASD risk alleles with low effect sizes work collectively in specific autoimmune disorders and show significant association statistics. In the genetic variation score test, we tested whether allele-specific associations between ASD and autoimmune disorders can be found using nominally significant single-nucleotide polymorphisms. In both tests, we found that ASD is probabilistically linked to ankylosing spondylitis (AS) and multiple sclerosis (MS). Association coefficients showed that ASD and AS were positively associated, meaning that autism susceptibility alleles may have a similar collective effect in AS. The association coefficients were negative between ASD and MS. Significant associations between ASD and two autoimmune disorders were identified. This genetic association supports the idea that specific immunological abnormalities may underlie the etiology of autism, at least in a number of cases

Photoswitchable Nanoparticles for Triggered Tissue Penetration and Drug Delivery

We report a novel nanoparticulate drug delivery system that undergoes reversible volume change from 150 to 40 nm upon phototriggering with UV light. The volume change of these monodisperse nanoparticles comprising spiropyran, which undergoes reversible photoisomerization, and PEGylated lipid enables repetitive dosing from a single administration and enhances tissue penetration. The photoswitching allows particles to fluoresce and release drugs inside cells when illuminated with UV light. The mechanism of the light-induced size switching and triggered-release is studied. These particles provide spatiotemporal control of drug release and enhanced tissue penetration, useful properties in many disease states including cancer

An unsupervised classification method for inferring original case locations from low-resolution disease maps

BACKGROUND: Widespread availability of geographic information systems software has facilitated the use of disease mapping in academia, government and private sector. Maps that display the address of affected patients are often exchanged in public forums, and published in peer-reviewed journal articles. As previously reported, a search of figure legends in five major medical journals found 19 articles from 1994–2004 that identify over 19,000 patient addresses. In this report, a method is presented to evaluate whether patient privacy is being breached in the publication of low-resolution disease maps. RESULTS: To demonstrate the effect, a hypothetical low-resolution map of geocoded patient addresses was created and the accuracy with which patient addresses can be resolved is described. Through georeferencing and unsupervised classification of the original image, the method precisely re-identified 26% (144/550) of the patient addresses from a presentation quality map and 79% (432/550) from a publication quality map. For the presentation quality map, 99.8% of the addresses were within 70 meters (approximately one city block length) of the predicted patient location, 51.6% of addresses were identified within five buildings, 70.7% within ten buildings and 93% within twenty buildings. For the publication quality map, all addresses were within 14 meters and 11 buildings of the predicted patient location. CONCLUSION: This study demonstrates that lowering the resolution of a map displaying geocoded patient addresses does not sufficiently protect patient addresses from re-identification. Guidelines to protect patient privacy, including those of medical journals, should reflect policies that ensure privacy protection when spatial data are displayed or published

An Epidemiological Network Model for Disease Outbreak Detection

Most surveillance systems are not robust to shifts in health care utilization. Ben Reis and colleagues developed network models that detected localized outbreaks better and were more robust to unpredictable shifts

Nanowired three-dimensional cardiac patches

Engineered cardiac patches for treating damaged heart tissues after a heart attack are normally produced by seeding heart cells within three-dimensional porous biomaterial scaffolds1, 2, 3. These biomaterials, which are usually made of either biological polymers such as alginate4 or synthetic polymers such as poly(lactic acid) (PLA)5, help cells organize into functioning tissues, but poor conductivity of these materials limits the ability of the patch to contract strongly as a unit6. Here, we show that incorporating gold nanowires within alginate scaffolds can bridge the electrically resistant pore walls of alginate and improve electrical communication between adjacent cardiac cells. Tissues grown on these composite matrices were thicker and better aligned than those grown on pristine alginate and when electrically stimulated, the cells in these tissues contracted synchronously. Furthermore, higher levels of the proteins involved in muscle contraction and electrical coupling are detected in the composite matrices. It is expected that the integration of conducting nanowires within three-dimensional scaffolds may improve the therapeutic value of current cardiac patches.National Institutes of Health (U.S.) (NIH, grant GM073626)National Institutes of Health (U.S.) (NIH, grant DE13023)National Institutes of Health (U.S.) (NIH, grant DE016516)American Heart Association (Postdoctoral Fellowship)National Institutes of Health (U.S.) (Ruth L. Kirschstein National Research Service Award (no. F32GM096546)

Divergent dysregulation of gene expression in murine models of fragile X syndrome and tuberous sclerosis

Background: Fragile X syndrome and tuberous sclerosis are genetic syndromes that both have a high rate of comorbidity with autism spectrum disorder (ASD). Several lines of evidence suggest that these two monogenic disorders may converge at a molecular level through the dysfunction of activity-dependent synaptic plasticity. Methods: To explore the characteristics of transcriptomic changes in these monogenic disorders, we profiled genome-wide gene expression levels in cerebellum and blood from murine models of fragile X syndrome and tuberous sclerosis. Results: Differentially expressed genes and enriched pathways were distinct for the two murine models examined, with the exception of immune response-related pathways. In the cerebellum of the Fmr1 knockout (Fmr1-KO) model, the neuroactive ligand receptor interaction pathway and gene sets associated with synaptic plasticity such as long-term potentiation, gap junction, and axon guidance were the most significantly perturbed pathways. The phosphatidylinositol signaling pathway was significantly dysregulated in both cerebellum and blood of Fmr1-KO mice. In Tsc2 heterozygous (+/−) mice, immune system-related pathways, genes encoding ribosomal proteins, and glycolipid metabolism pathways were significantly changed in both tissues. Conclusions: Our data suggest that distinct molecular pathways may be involved in ASD with known but different genetic causes and that blood gene expression profiles of Fmr1-KO and Tsc2+/− mice mirror some, but not all, of the perturbed molecular pathways in the brain

Peripheral blood gene expression signature differentiates children with autism from unaffected siblings

Autism spectrum disorder (ASD) is one of the most prevalent neurodevelopmental disorders with high heritability, yet a majority of genetic contribution to pathophysiology is not known. Siblings of individuals with ASD are at increased risk for ASD and autistic traits, but the genetic contribution for simplex families is estimated to be less when compared to multiplex families. To explore the genomic (dis-) similarity between proband and unaffected sibling in simplex families, we used genome-wide gene expression profiles of blood from 20 proband-unaffected sibling pairs and 18 unrelated control individuals. The global gene expression profiles of unaffected siblings were more similar to those from probands as they shared genetic and environmental background. A total of 189 genes were significantly differentially expressed between proband-sib pairs (nominal p < 0.01) after controlling for age, sex, and family effects. Probands and siblings were distinguished into two groups by cluster analysis with these genes. Overall, unaffected siblings were equally distant from the centroid of probands and from that of unrelated controls with the differentially expressed genes. Interestingly, five of 20 siblings had gene expression profiles that were more similar to unrelated controls than to their matched probands. In summary, we found a set of genes that distinguished probands from the unaffected siblings, and a subgroup of unaffected siblings who were more similar to probands. The pathways that characterized probands compared to siblings using peripheral blood gene expression profiles were the up-regulation of ribosomal, spliceosomal, and mitochondrial pathways, and the down-regulation of neuroreceptor-ligand, immune response and calcium signaling pathways. Further integrative study with structural genetic variations such as de novo mutations, rare variants, and copy number variations would clarify whether these transcriptomic changes are structural or environmental in origin.Simons Foundatio

Early Detection of Poor Adherers to Statins: Applying Individualized Surveillance to Pay for Performance

Background: Medication nonadherence costs $300 billion annually in the US. Medicare Advantage plans have a financial incentive to increase medication adherence among members because the Centers for Medicare and Medicaid Services (CMS) now awards substantive bonus payments to such plans, based in part on population adherence to chronic medications. We sought to build an individualized surveillance model that detects early which beneficiaries will fall below the CMS adherence threshold. Methods: This was a retrospective study of over 210,000 beneficiaries initiating statins, in a database of private insurance claims, from 2008-2011. A logistic regression model was constructed to use statin adherence from initiation to day 90 to predict beneficiaries who would not meet the CMS measure of proportion of days covered 0.8 or above, from day 91 to 365. The model controlled for 15 additional characteristics. In a sensitivity analysis, we varied the number of days of adherence data used for prediction. Results: Lower adherence in the first 90 days was the strongest predictor of one-year nonadherence, with an odds ratio of 25.0 (95% confidence interval 23.7-26.5) for poor adherence at one year. The model had an area under the receiver operating characteristic curve of 0.80. Sensitivity analysis revealed that predictions of comparable accuracy could be made only 40 days after statin initiation. When members with 30-day supplies for their first statin fill had predictions made at 40 days, and members with 90-day supplies for their first fill had predictions made at 100 days, poor adherence could be predicted with 86% positive predictive value. Conclusions: To preserve their Medicare Star ratings, plan managers should identify or develop effective programs to improve adherence. An individualized surveillance approach can be used to target members who would most benefit, recognizing the tradeoff between improved model performance over time and the advantage of earlier detection