73 research outputs found
Enhanced Proliferation of Monolayer Cultures of Embryonic Stem (ES) Cell-Derived Cardiomyocytes Following Acute Loss of Retinoblastoma
Background: Cardiomyocyte (CM) cell cycle analysis has been impeded because of a reliance on primary neonatal cultures of poorly proliferating cells or chronic transgenic animal models with innate compensatory mechanisms. Methodology/Principal Findings: We describe an in vitro model consisting of monolayer cultures of highly proliferative embryonic stem (ES) cell-derived CM. Following induction with ascorbate and selection with puromycin, early CM cultures are.98 % pure, and at least 85 % of the cells actively proliferate. During the proliferative stage, cells express high levels of E2F3a, B-Myb and phosphorylated forms of retinoblastoma (Rb), but with continued cultivation, cells stop dividing and mature functionally. This developmental transition is characterized by a switch from slow skeletal to cardiac TnI, an increase in binucleation, cardiac calsequestrin and hypophosphorylated Rb, a decrease in E2F3, B-Myb and atrial natriuretic factor, and the establishment of a more negative resting membrane potential. Although previous publications suggested that Rb was not necessary for cell cycle control in heart, we find following acute knockdown of Rb that this factor actively regulates progression through the G1 checkpoint and that its loss promotes proliferation at the expense of CM maturation. Conclusions/Significance: We have established a unique model system for studying cardiac cell cycle progression, and show in contrast to previous reports that Rb actively regulates both cell cycle progression through the G1 checkpoint an
CodingQuarry: Highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts
Background: The impact of gene annotation quality on functional and comparative genomics makes gene prediction an important process, particularly in non-model species, including many fungi. Sets of homologous protein sequences are rarely complete with respect to the fungal species of interest and are often small or unreliable, especially when closely related species have not been sequenced or annotated in detail. In these cases, protein homology-based evidence fails to correctly annotate many genes, or significantly improve ab initio predictions. Generalised hidden Markov models (GHMM) have proven to be invaluable tools in gene annotation and, recently, RNA-seq has emerged as a cost-effective means to significantly improve the quality of automated gene annotation. As these methods do not require sets of homologous proteins, improving gene prediction from these resources is of benefit to fungal researchers. While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study. Results: CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. Our approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. Stringent benchmarking against high-confidence annotation subsets showed CodingQuarry predicted 91.3% of Schizosaccharomyces pombe genes and 90.4% of Saccharomyces cerevisiae genes perfectly. These results are 4-5% better than those of AUGUSTUS, the next best performing RNA-seq driven gene predictor tested. Comparisons against whole genome Sc. pombe and S. cerevisiae annotations further substantiate a 4-5% improvement in the number of correctly predicted genes. Conclusions: We demonstrate the success of a novel method of incorporating RNA-seq data into GHMM fungal gene prediction. This shows that a high quality annotation can be achieved without relying on protein homology or a training set of genes. CodingQuarry is freely available (https://sourceforge.net/projects/codingquarry/), and suitable for incorporation into genome annotation pipelines
Treatment of rheumatoid arthritis: a global perspective on the use of antirheumatic drugs
Modern therapy for rheumatoid arthritis (RA) is based on knowledge of the severity of the natural history of the disease. RA patients are approached with early and aggressive treatment strategies, methotrexate as an anchor drug, biological targeted therapies in those with inadequate response to methotrexate, and “tight control,” aiming for remission and low disease activity according to quantitative monitoring. This chapter presents a rationale for current treatment strategies for RA with antirheumatic drugs, a review of published reports concerning treatments in clinical cohorts outside of clinical trials, and current treatments at 61 sites in 21 countries in the QUEST-RA database
Host Immune Responses to a Viral Immune Modulating Protein: Immunogenicity of Viral Interleukin-10 in Rhesus Cytomegalovirus-Infected Rhesus Macaques
, consistent with a central role for rhcmvIL-10 during acute virus-host interactions. Since cmvIL-10 and rhcmvIL-10 are extremely divergent from the cIL-10 of their respective hosts, vaccine-mediated neutralization of their function could inhibit establishment of viral persistence without inhibition of cIL-10.As a prelude to evaluating cmvIL-10-based vaccines in humans, the rhesus macaque model of HCMV was used to interrogate peripheral and mucosal immune responses to rhcmvIL-10 in RhCMV-infected animals. ELISA were used to detect rhcmvIL-10-binding antibodies in plasma and saliva, and an IL-12-based bioassay was used to quantify plasma antibodies that neutralized rhcmvIL-10 function. rhcmvIL-10 is highly immunogenic during RhCMV infection, stimulating high avidity rhcmvIL-10-binding antibodies in the plasma of all infected animals. Most infected animals also exhibited plasma antibodies that partially neutralized rhcmvIL-10 function but did not cross-neutralize the function of rhesus cIL-10. Notably, minimally detectable rhcmvIL-10-binding antibodies were detected in saliva.This study demonstrates that rhcmvIL-10, as a surrogate for cmvIL-10, is a viable vaccine candidate because (1) it is highly immunogenic during natural RhCMV infection, and (2) neutralizing antibodies to rhcmvIL-10 do not cross-react with rhesus cIL-10. Exceedingly low rhcmvIL-10 antibodies in saliva further suggest that the oral mucosa, which is critical in RhCMV natural history, is associated with suboptimal anti-rhcmvIL-10 antibody responses
Differences in T cell cytotoxicity and cell death mechanisms between progressive multifocal leukoencephalopathy, herpes simplex virus encephalitis and cytomegalovirus encephalitis
The contribution of X-linked coding variation to severe developmental disorders
Over 130 X-linked genes have been robustly associated with developmental disorders, and X-linked causes have been hypothesised to underlie the higher developmental disorder rates in males. Here, we evaluate the burden of X-linked coding variation in 11,044 developmental disorder patients, and find a similar rate of X-linked causes in males and females (6.0% and 6.9%, respectively), indicating that such variants do not account for the 1.4-fold male bias. We develop an improved strategy to detect X-linked developmental disorders and identify 23 significant genes, all of which were previously known, consistent with our inference that the vast majority of the X-linked burden is in known developmental disorder-associated genes. Importantly, we estimate that, in male probands, only 13% of inherited rare missense variants in known developmental disorder-associated genes are likely to be pathogenic. Our results demonstrate that statistical analysis of large datasets can refine our understanding of modes of inheritance for individual X-linked disorders
Multi-messenger observations of a binary neutron star merger
On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta
The Gene Ontology: enhancements for 2011
The Gene Ontology (GO) (http://www.geneontology.org) is a community bioinformatics resource that represents gene product function through the use of structured, controlled vocabularies. The number of GO annotations of gene products has increased due to curation efforts among GO Consortium (GOC) groups, including focused literature-based annotation and ortholog-based functional inference. The GO ontologies continue to expand and improve as a result of targeted ontology development, including the introduction of computable logical definitions and development of new tools for the streamlined addition of terms to the ontology. The GOC continues to support its user community through the use of e-mail lists, social media and web-based resources
Non-coding variants upstream of MEF2C cause severe developmental disorder through three distinct loss-of-function mechanisms
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