72 research outputs found
Intensity dependent estimation of noise in microarrays improves detection of differentially expressed genes
Coupled pre-mRNA and mRNA dynamics unveil operational strategies underlying transcriptional responses to stimuli
Genome-wide simultaneous measurements of pre-mRNA and mRNA expression reveal unexpected time-dependent transcript production and degradation profiles in response to external stimulus, as well as a striking lack of concordance between mRNA abundance and transcript production profiles
Identification of novel DNA-damage tolerance genes reveals regulation of translesion DNA synthesis by nucleophosmin
Cells cope with replication-blocking lesions via translesion DNA synthesis (TLS). TLS is carried out by low-fidelity DNA polymerases that replicate across lesions, thereby preventing genome instability at the cost of increased point mutations. Here we perform a twostage siRNA-based functional screen for mammalian TLS genes and identify 17 validated TLS genes. One of the genes, NPM1, is frequently mutated in acute myeloid leukaemia (AML). We show that NPM1 (nucleophosmin) regulates TLS via interaction with the catalytic core of DNA polymerase-eta (pol eta), and that NPM1 deficiency causes a TLS defect due to proteasomal degradation of pol eta. Moreover, the prevalent NPM1c+ mutation that causes NPM1 mislocalization in similar to 30% of AML patients results in excessive degradation of pol eta. These results establish the role of NPM1 as a key TLS regulator, and suggest a mechanism for the better prognosis of AML patients carrying mutations in NPM1
Identification of novel DNA-damage tolerance genes reveals regulation of translesion DNA synthesis by nucleophosmin
Cells cope with replication-blocking lesions via translesion DNA synthesis (TLS). TLS is carried out by low-fidelity DNA polymerases that replicate across lesions, thereby preventing genome instability at the cost of increased point mutations. Here we perform a twostage siRNA-based functional screen for mammalian TLS genes and identify 17 validated TLS genes. One of the genes, NPM1, is frequently mutated in acute myeloid leukaemia (AML). We show that NPM1 (nucleophosmin) regulates TLS via interaction with the catalytic core of DNA polymerase-eta (pol eta), and that NPM1 deficiency causes a TLS defect due to proteasomal degradation of pol eta. Moreover, the prevalent NPM1c+ mutation that causes NPM1 mislocalization in similar to 30% of AML patients results in excessive degradation of pol eta. These results establish the role of NPM1 as a key TLS regulator, and suggest a mechanism for the better prognosis of AML patients carrying mutations in NPM1
Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells.
Understanding human embryonic ventral midbrain is of major interest for Parkinson's disease. However, the cell types, their gene expression dynamics, and their relationship to commonly used rodent models remain to be defined. We performed single-cell RNA sequencing to examine ventral midbrain development in human and mouse. We found 25 molecularly defined human cell types, including five subtypes of radial glia-like cells and four progenitors. In the mouse, two mature fetal dopaminergic neuron subtypes diversified into five adult classes during postnatal development. Cell types and gene expression were generally conserved across species, but with clear differences in cell proliferation, developmental timing, and dopaminergic neuron development. Additionally, we developed a method to quantitatively assess the fidelity of dopaminergic neurons derived from human pluripotent stem cells, at a single-cell level. Thus, our study provides insight into the molecular programs controlling human midbrain development and provides a foundation for the development of cell replacement therapies.All authors were supported by EU FP7 grant DDPDGENES. S.L. was supported by European Research Council grant 261063 (BRAINCELL), Knut and Alice Wallenberg Foundation grant 2015.0041, Swedish Research Council (STARGET), and the Swedish Foundation for Strategic Research (RIF14-0057). A.Z. was supported by the Human Frontier Science Program. E.A. was supported by Swedish Research Council (VR projects: 2011-3116 and 2011-3318), Swedish Foundation for Strategic Research (SRL program), and Karolinska Institutet (SFO Thematic Center in Stem cells and Regenerative Medicine). E.A. and R.A.B. were supported by the EU FP7 grant NeuroStemcellRepair. R.A.B. was also supported by an NIHR Biomedical Research Centre award to the University of Cambridge/Addenbrookes Hospital. iCell dopaminergic neurons were a generous gift from Cellular Dynamics International. Single-cell RNA-seq servic0es were provided by the Eukaryotic Single-cell Genomics facility and the National Genomics Infrastructure at Science for Life Laboratory.This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.cell.2016.09.02
Transcriptional Dynamics Reveal Critical Roles for Non-coding RNAs in the Immediate-Early Response
<div><p>The immediate-early response mediates cell fate in response to a variety of extracellular stimuli and is dysregulated in many cancers. However, the specificity of the response across stimuli and cell types, and the roles of non-coding RNAs are not well understood. Using a large collection of densely-sampled time series expression data we have examined the induction of the immediate-early response in unparalleled detail, across cell types and stimuli. We exploit cap analysis of gene expression (CAGE) time series datasets to directly measure promoter activities over time. Using a novel analysis method for time series data we identify transcripts with expression patterns that closely resemble the dynamics of known immediate-early genes (IEGs) and this enables a comprehensive comparative study of these genes and their chromatin state. Surprisingly, these data suggest that the earliest transcriptional responses often involve promoters generating non-coding RNAs, many of which are produced in advance of canonical protein-coding IEGs. IEGs are known to be capable of induction without de novo protein synthesis. Consistent with this, we find that the response of both protein-coding and non-coding RNA IEGs can be explained by their transcriptionally poised, permissive chromatin state prior to stimulation. We also explore the function of non-coding RNAs in the attenuation of the immediate early response in a small RNA sequencing dataset matched to the CAGE data: We identify a novel set of microRNAs responsible for the attenuation of the IEG response in an estrogen receptor positive cancer cell line. Our computational statistical method is well suited to meta-analyses as there is no requirement for transcripts to pass thresholds for significant differential expression between time points, and it is agnostic to the number of time points per dataset.</p></div
Intensity dependent estimation of noise in microarrays improves detection of differentially expressed genes
Abstract Background In many microarray experiments, analysis is severely hindered by a major difficulty: the small number of samples for which expression data has been measured. When one searches for differentially expressed genes, the small number of samples gives rise to an inaccurate estimation of the experimental noise. This, in turn, leads to loss of statistical power. Results We show that the measurement noise of genes with similar expression levels (intensity) is identically and independently distributed, and that this (intensity dependent) distribution is approximately normal. Our method can be easily adapted and used to test whether these statement hold for data from any particular microarray experiment. We propose a method that provides an accurate estimation of the intensity-dependent variance of the noise distribution, and demonstrate that using this estimation we can detect differential expression with much better statistical power than that of standard t-test, and can compare the noise levels of different experiments and platforms. Conclusions When the number of samples is small, the simple method we propose improves significantly the statistical power in identifying differentially expressed genes.</p
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