38 research outputs found
A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in Streptomyces coelicolor
BACKGROUND: Identification of coordinately regulated genes according to the level of their expression during the time course of a process allows for discovering functional relationships among genes involved in the process. RESULTS: We present a single class classification method for the identification of genes of similar function from a gene expression time series. It is based on a parallel genetic algorithm which is a supervised computer learning method exploiting prior knowledge of gene function to identify unknown genes of similar function from expression data. The algorithm was tested with a set of randomly generated patterns; the results were compared with seven other classification algorithms including support vector machines. The algorithm avoids several problems associated with unsupervised clustering methods, and it shows better performance then the other algorithms. The algorithm was applied to the identification of secondary metabolite gene clusters of the antibiotic-producing eubacterium Streptomyces coelicolor. The algorithm also identified pathways associated with transport of the secondary metabolites out of the cell. We used the method for the prediction of the functional role of particular ORFs based on the expression data. CONCLUSION: Through analysis of a time series of gene expression, the algorithm identifies pathways which are directly or indirectly associated with genes of interest, and which are active during the time course of the experiment
Supervised inference of gene-regulatory networks
<p>Abstract</p> <p>Background</p> <p>Inference of protein interaction networks from various sources of data has become an important topic of both systems and computational biology. Here we present a supervised approach to identification of gene expression regulatory networks.</p> <p>Results</p> <p>The method is based on a kernel approach accompanied with genetic programming. As a data source, the method utilizes gene expression time series for prediction of interactions among regulatory proteins and their target genes. The performance of the method was verified using Saccharomyces cerevisiae cell cycle and DNA/RNA/protein biosynthesis gene expression data. The results were compared with independent data sources. Finally, a prediction of novel interactions within yeast gene expression circuits has been performed.</p> <p>Conclusion</p> <p>Results show that our algorithm gives, in most cases, results identical with the independent experiments, when compared with the YEASTRACT database. In several cases our algorithm gives predictions of novel interactions which have not been reported.</p
Global, regional, and national burden of stroke, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016
Summary
Background Stroke is a leading cause of mortality and disability worldwide and the economic costs of treatment and
post-stroke care are substantial. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a
systematic, comparable method of quantifying health loss by disease, age, sex, year, and location to provide information
to health systems and policy makers on more than 300 causes of disease and injury, including stroke. The results
presented here are the estimates of burden due to overall stroke and ischaemic and haemorrhagic stroke from
GBD 2016.
Methods We report estimates and corresponding uncertainty intervals (UIs), from 1990 to 2016, for incidence,
prevalence, deaths, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years
(DALYs). DALYs were generated by summing YLLs and YLDs. Cause-specific mortality was estimated using an
ensemble modelling process with vital registration and verbal autopsy data as inputs. Non-fatal estimates were
generated using Bayesian meta-regression incorporating data from registries, scientific literature, administrative
records, and surveys. The Socio-demographic Index (SDI), a summary indicator generated using educational
attainment, lagged distributed income, and total fertility rate, was used to group countries into quintiles.
Findings In 2016, there were 5·5 million (95% UI 5·3 to 5·7) deaths and 116·4 million (111·4 to 121·4) DALYs due to
stroke. The global age-standardised mortality rate decreased by 36·2% (–39·3 to –33·6) from 1990 to 2016, with
decreases in all SDI quintiles. Over the same period, the global age-standardised DALY rate declined by 34·2%
(–37·2 to –31·5), also with decreases in all SDI quintiles. There were 13·7 million (12·7 to 14·7) new stroke cases in
2016. Global age-standardised incidence declined by 8·1% (–10·7 to –5·5) from 1990 to 2016 and decreased in all SDI
quintiles except the middle SDI group. There were 80·1 million (74·1 to 86·3) prevalent cases of stroke globally in
2016; 41·1 million (38·0 to 44·3) in women and 39·0 million (36·1 to 42·1) in men.
Interpretation Although age-standardised mortality rates have decreased sharply from 1990 to 2016, the decrease in
age-standardised incidence has been less steep, indicating that the burden of stroke is likely to remain high. Planned
updates to future GBD iterations include generating separate estimates for subarachnoid haemorrhage and
intracerebral haemorrhage, generating estimates of transient ischaemic attack, and including atrial fibrillation as a
risk factor
Stage-specific regulation of the WNT/β-catenin pathway enhances differentiation of hESCs into hepatocytes.
Background & aimsHepatocytes differentiated from human embryonic stem cells (hESCs) have the potential to overcome the shortage of primary hepatocytes for clinical use and drug development. Many strategies for this process have been reported, but the functionality of the resulting cells is incomplete. We hypothesize that the functionality of hPSC-derived hepatocytes might be improved by making the differentiation method more similar to normal in vivo hepatic development.MethodsWe tested combinations of growth factors and small molecules targeting candidate signaling pathways culled from the literature to identify optimal conditions for differentiation of hESCs to hepatocytes, using qRT-PCR for stage-specific markers to identify the best conditions. Immunocytochemistry was then used to validate the selected conditions. Finally, induction of expression of metabolic enzymes in terminally differentiated cells was used to assess the functionality of the hESC-derived hepatocytes.ResultsOptimal differentiation of hESCs was attained using a 5-stage protocol. After initial induction of definitive endoderm (stage 1), we showed that inhibition of the WNT/β-catenin pathway during the 2nd and 3rd stages of differentiation was required to specify first posterior foregut, and then hepatic gut cells. In contrast, during the 4th stage of differentiation, we found that activation of the WNT/β-catenin pathway allowed generation of proliferative bipotent hepatoblasts, which then were efficiently differentiated into hepatocytes in the 5th stage by dual inhibition of TGF-β and NOTCH signaling.ConclusionHere, we show that stage-specific regulation of the WNT/β-catenin pathway results in improved differentiation of hESCs to functional hepatocytes
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Identification of Subtype-Specific Markers for Preeclampsia Using Placental Pathology and RNAseq.
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Miniaturization Technologies for Efficient Single-Cell Library Preparation for Next-Generation Sequencing.
As the cost of next-generation sequencing has decreased, library preparation costs have become a more significant proportion of the total cost, especially for high-throughput applications such as single-cell RNA profiling. Here, we have applied novel technologies to scale down reaction volumes for library preparation. Our system consisted of in vitro differentiated human embryonic stem cells representing two stages of pancreatic differentiation, for which we prepared multiple biological and technical replicates. We used the Fluidigm (San Francisco, CA) C1 single-cell Autoprep System for single-cell complementary DNA (cDNA) generation and an enzyme-based tagmentation system (Nextera XT; Illumina, San Diego, CA) with a nanoliter liquid handler (mosquito HTS; TTP Labtech, Royston, UK) for library preparation, reducing the reaction volume down to 2 µL and using as little as 20 pg of input cDNA. The resulting sequencing data were bioinformatically analyzed and correlated among the different library reaction volumes. Our results showed that decreasing the reaction volume did not interfere with the quality or the reproducibility of the sequencing data, and the transcriptional data from the scaled-down libraries allowed us to distinguish between single cells. Thus, we have developed a process to enable efficient and cost-effective high-throughput single-cell transcriptome sequencing
Miniaturization Technologies for Efficient Single-Cell Library Preparation for Next-Generation Sequencing.
As the cost of next-generation sequencing has decreased, library preparation costs have become a more significant proportion of the total cost, especially for high-throughput applications such as single-cell RNA profiling. Here, we have applied novel technologies to scale down reaction volumes for library preparation. Our system consisted of in vitro differentiated human embryonic stem cells representing two stages of pancreatic differentiation, for which we prepared multiple biological and technical replicates. We used the Fluidigm (San Francisco, CA) C1 single-cell Autoprep System for single-cell complementary DNA (cDNA) generation and an enzyme-based tagmentation system (Nextera XT; Illumina, San Diego, CA) with a nanoliter liquid handler (mosquito HTS; TTP Labtech, Royston, UK) for library preparation, reducing the reaction volume down to 2 µL and using as little as 20 pg of input cDNA. The resulting sequencing data were bioinformatically analyzed and correlated among the different library reaction volumes. Our results showed that decreasing the reaction volume did not interfere with the quality or the reproducibility of the sequencing data, and the transcriptional data from the scaled-down libraries allowed us to distinguish between single cells. Thus, we have developed a process to enable efficient and cost-effective high-throughput single-cell transcriptome sequencing
Icariin reduces bone loss in a Rankl-induced transgenic medaka (Oryzias latipes) model for osteoporosis
10.1111/jfb.14241JOURNAL OF FISH BIOLOGY9841039-104
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Molecular signatures associated with successful implantation of the human blastocyst
Abstract:
Embryo implantation in humans is remarkably inefficient for reasons that remain largely unexplained, and high rates of implantation failure remain one of the greatest obstacles in treating infertility. The volume of gene expression data available from human embryos has rapidly accumulated in recent years. However, prioritization of these data to identify the subset of genes that determine successful implantation remains a challenge, in part, because comprehensive analyses cannot be performed on the same embryos that are transferred. Here, we leverage clinical morphologic grading—known for decades to correlate with implantation potential—and transcriptome analyses of matched embryonic and abembryonic samples to identify genes and cell-cell interactions enriched and depleted in human blastocysts of good and poor morphology, genome-wide. Unexpectedly, we discovered that the greatest molecular difference was in the state of the extraembryonic primitive endoderm (PrE), with relative deficiencies in PrE development in embryos of poor morphology at the time of embryo transfer. Together, our results support a model in which implantation success is most strongly reflected by factors and signals from the embryonic compartment and suggest that deficiencies in PrE development, in particular, are common among embryos with reduced implantation potential. Our study provides a valuable resource for those investigating the markers and mechanisms of human embryo implantation