The Escherichia coli transcriptome mostly consists of independently regulated modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome

Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence

<p>Abstract</p> <p>Background</p> <p>People with diabetes can suffer from diverse complications that seriously erode quality of life. Diabetes, costing the United States more than $174 billion per year in 2007, is expected to take an increasingly large financial toll in subsequent years. Accurate projections of diabetes burden are essential to policymakers planning for future health care needs and costs.</p> <p>Methods</p> <p>Using data on prediabetes and diabetes prevalence in the United States, forecasted incidence, and current US Census projections of mortality and migration, the authors constructed a series of dynamic models employing systems of difference equations to project the future burden of diabetes among US adults. A three-state model partitions the US population into no diabetes, undiagnosed diabetes, and diagnosed diabetes. A four-state model divides the state of "no diabetes" into high-risk (prediabetes) and low-risk (normal glucose) states. A five-state model incorporates an intervention designed to prevent or delay diabetes in adults at high risk.</p> <p>Results</p> <p>The authors project that annual diagnosed diabetes incidence (new cases) will increase from about 8 cases per 1,000 in 2008 to about 15 in 2050. Assuming low incidence and relatively high diabetes mortality, total diabetes prevalence (diagnosed and undiagnosed cases) is projected to increase from 14% in 2010 to 21% of the US adult population by 2050. However, if recent increases in diabetes incidence continue and diabetes mortality is relatively low, prevalence will increase to 33% by 2050. A middle-ground scenario projects a prevalence of 25% to 28% by 2050. Intervention can reduce, but not eliminate, increases in diabetes prevalence.</p> <p>Conclusions</p> <p>These projected increases are largely attributable to the aging of the US population, increasing numbers of members of higher-risk minority groups in the population, and people with diabetes living longer. Effective strategies will need to be undertaken to moderate the impact of these factors on national diabetes burden. Our analysis suggests that widespread implementation of reasonably effective preventive interventions focused on high-risk subgroups of the population can considerably reduce, but not eliminate, future increases in diabetes prevalence.</p

Rescue of Dystrophic Skeletal Muscle by PGC-1α Involves a Fast to Slow Fiber Type Shift in the mdx Mouse

Increased utrophin expression is known to reduce pathology in dystrophin-deficient skeletal muscles. Transgenic over-expression of PGC-1α has been shown to increase levels of utrophin mRNA and improve the histology of mdx muscles. Other reports have shown that PGC-1α signaling can lead to increased oxidative capacity and a fast to slow fiber type shift. Given that it has been shown that slow fibers produce and maintain more utrophin than fast skeletal muscle fibers, we hypothesized that over-expression of PGC-1α in post-natal mdx mice would increase utrophin levels via a fiber type shift, resulting in more slow, oxidative fibers that are also more resistant to contraction-induced damage. To test this hypothesis, neonatal mdx mice were injected with recombinant adeno-associated virus (AAV) driving expression of PGC-1α. PGC-1α over-expression resulted in increased utrophin and type I myosin heavy chain expression as well as elevated mitochondrial protein expression. Muscles were shown to be more resistant to contraction-induced damage and more fatigue resistant. Sirt-1 was increased while p38 activation and NRF-1 were reduced in PGC-1α over-expressing muscle when compared to control. We also evaluated if the use a pharmacological PGC-1α pathway activator, resveratrol, could drive the same physiological changes. Resveratrol administration (100 mg/kg/day) resulted in improved fatigue resistance, but did not achieve significant increases in utrophin expression. These data suggest that the PGC-1α pathway is a potential target for therapeutic intervention in dystrophic skeletal muscle

JBrowse: a dynamic web platform for genome visualization and analysis

BACKGROUND: JBrowse is a fast and full-featured genome browser built with JavaScript and HTML5. It is easily embedded into websites or apps but can also be served as a standalone web page. RESULTS: Overall improvements to speed and scalability are accompanied by specific enhancements that support complex interactive queries on large track sets. Analysis functions can readily be added using the plugin framework; most visual aspects of tracks can also be customized, along with clicks, mouseovers, menus, and popup boxes. JBrowse can also be used to browse local annotation files offline and to generate high-resolution figures for publication. CONCLUSIONS: JBrowse is a mature web application suitable for genome visualization and analysis

Computational solutions for omics data

High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.National Institutes of Health (U.S.) (Grant GM081871

Global Identification and Characterization of Transcriptionally Active Regions in the Rice Genome

Genome tiling microarray studies have consistently documented rich transcriptional activity beyond the annotated genes. However, systematic characterization and transcriptional profiling of the putative novel transcripts on the genome scale are still lacking. We report here the identification of 25,352 and 27,744 transcriptionally active regions (TARs) not encoded by annotated exons in the rice (Oryza. sativa) subspecies japonica and indica, respectively. The non-exonic TARs account for approximately two thirds of the total TARs detected by tiling arrays and represent transcripts likely conserved between japonica and indica. Transcription of 21,018 (83%) japonica non-exonic TARs was verified through expression profiling in 10 tissue types using a re-array in which annotated genes and TARs were each represented by five independent probes. Subsequent analyses indicate that about 80% of the japonica TARs that were not assigned to annotated exons can be assigned to various putatively functional or structural elements of the rice genome, including splice variants, uncharacterized portions of incompletely annotated genes, antisense transcripts, duplicated gene fragments, and potential non-coding RNAs. These results provide a systematic characterization of non-exonic transcripts in rice and thus expand the current view of the complexity and dynamics of the rice transcriptome

Effect of sulodexide on endothelial glycocalyx and vascular permeability in patients with type 2 diabetes mellitus

Endothelial glycocalyx perturbation contributes to increased vascular permeability. In the present study we set out to evaluate whether: (1) glycocalyx is perturbed in individuals with type 2 diabetes mellitus, and (2) oral glycocalyx precursor treatment improves glycocalyx properties. Male participants with type 2 diabetes (n = 10) and controls (n = 10) were evaluated before and after 2 months of sulodexide administration (200 mg/day). The glycocalyx dimension was estimated in two different vascular beds using sidestream dark field imaging and combined fluorescein/indocyanine green angiography for sublingual and retinal vessels, respectively. Transcapillary escape rate of albumin (TER(alb)) and hyaluronan catabolism were assessed as measures of vascular permeability. Both sublingual dimensions (0.64 [0.57-0.75] μm vs 0.78 [0.71-0.85] μm, p <0.05, medians [interquartile range]) and retinal glycocalyx dimensions (5.38 [4.88-6.59] μm vs 8.89 [4.74-11.84] μm, p <0.05) were reduced in the type 2 diabetes group compared with the controls whereas TER(alb) was increased (5.6 ± 2.3% vs 3.7 ± 1.7% in the controls, p <0.05). In line with these findings, markers of hyaluronan catabolism were increased with diabetes (hyaluronan 137 ± 29 vs 81 ± 8 ng/ml and hyaluronidase 78 ± 4 vs 67 ± 2 U/ml, both p <0.05). Sulodexide increased both the sublingual and retinal glycocalyx dimensions in participants with diabetes (to 0.93 [0.83-0.99] μm and to 5.88 [5.33-6.26] μm, respectively, p <0.05). In line, a trend towards TER(alb) normalisation (to 4.0 ± 2.3%) and decreases in plasma hyaluronidase (to 72 ± 2 U/ml, p <0.05) were observed in the diabetes group. Type 2 diabetes is associated with glycocalyx perturbation and increased vascular permeability, which are partially restored following sulodexide administration. Further studies are warranted to determine whether long-term treatment with sulodexide has a beneficial effect on cardiovascular risk. www.trialregister.nl NTR780/ http://isrctn.org ISRCTN82695186 An unrestricted Novartis Foundation for Cardiovascular Excellence grant (2006) to M. Nieuwdorp/E. S. G. Stroes, Dutch Heart Foundation (grant number 2005T037

Genome-Wide Analyses of Recombination Prone Regions Predict Role of DNA Structural Motif in Recombination

HapMap findings reveal surprisingly asymmetric distribution of recombinogenic regions. Short recombinogenic regions (hotspots) are interspersed between large relatively non-recombinogenic regions. This raises the interesting possibility of DNA sequence and/or other cis- elements as determinants of recombination. We hypothesized the involvement of non-canonical sequences that can result in local non-B DNA structures and tested this using the G-quadruplex DNA as a model. G-quadruplex or G4 DNA is a unique form of four-stranded non-B DNA structure that engages certain G-rich sequences, presence of such motifs has been noted within telomeres. In support of this hypothesis, genome-wide computational analyses presented here reveal enrichment of potential G4 (PG4) DNA forming sequences within 25618 human hotspots relative to 9290 coldspots (p<0.0001). Furthermore, co-occurrence of PG4 DNA within several short sequence elements that are associated with recombinogenic regions was found to be significantly more than randomly expected. Interestingly, analyses of more than 50 DNA binding factors revealed that co-occurrence of PG4 DNA with target DNA binding sites of transcription factors c-Rel, NF-kappa B (p50 and p65) and Evi-1 was significantly enriched in recombination-prone regions. These observations support involvement of G4 DNA in recombination, predicting a functional model that is consistent with duplex-strand separation induced by formation of G4 motifs in supercoiled DNA and/or when assisted by other cellular factors

A Conserved PHD Finger Protein and Endogenous RNAi Modulate Insulin Signaling in Caenorhabditis elegans

Insulin signaling has a profound effect on longevity and the oxidative stress resistance of animals. Inhibition of insulin signaling results in the activation of DAF-16/FOXO and SKN-1/Nrf transcription factors and increased animal fitness. By studying the biological functions of the endogenous RNA interference factor RDE-4 and conserved PHD zinc finger protein ZFP-1 (AF10), which regulate overlapping sets of genes in Caenorhabditis elegans, we identified an important role for these factors in the negative modulation of transcription of the insulin/PI3 signaling-dependent kinase PDK-1. Consistently, increased expression of pdk-1 in zfp-1 and rde-4 mutants contributed to their reduced lifespan and sensitivity to oxidative stress and pathogens due to the reduction in the expression of DAF-16 and SKN-1 targets. We found that the function of ZFP-1 in modulating pdk-1 transcription was important for the extended lifespan of the age-1(hx546) reduction-of-function PI3 kinase mutant, since the lifespan of the age-1; zfp-1 double mutant strain was significantly shorter compared to age-1(hx546). We further demonstrate that overexpression of ZFP-1 caused an increased resistance to oxidative stress in a DAF-16–dependent manner. Our findings suggest that epigenetic regulation of key upstream signaling components in signal transduction pathways through chromatin and RNAi may have a large impact on the outcome of signaling and expression of numerous downstream genes.Leukemia & Lymphoma Society of America (3260-07 Special Fellow Award)Arnold and Mabel Beckman Foundation (Young Investigator Award)United States. National Institutes of Health (Director's New Innovator Award (1 DP2 OD006412-01))United States. National Institutes of Health (grant GM66269)modENCODE (grant U01 HG004270)United States. National Institutes of Health (training grant 5T32 GM07088-34