Cellular decision-making bias: the missing ingredient in cell functional diversity

Cell functional diversity is a significant determinant on how biological processes unfold. Most accounts of diversity involve a search for sequence or expression differences. Perhaps there are more subtle mechanisms at work. Using the metaphor of information processing and decision-making might provide a clearer view of these subtleties. Understanding adaptive and transformative processes (such as cellular reprogramming) as a series of simple decisions allows us to use a technique called cellular signal detection theory (cellular SDT) to detect potential bias in mechanisms that favor one outcome over another. We can apply method of detecting cellular reprogramming bias to cellular reprogramming and other complex molecular processes. To demonstrate scope of this method, we will critically examine differences between cell phenotypes reprogrammed to muscle fiber and neuron phenotypes. In cases where the signature of phenotypic bias is cryptic, signatures of genomic bias (pre-existing and induced) may provide an alternative. The examination of these alternates will be explored using data from a series of fibroblast cell lines before cellular reprogramming (pre-existing) and differences between fractions of cellular RNA for individual genes after drug treatment (induced). In conclusion, the usefulness and limitations of this method and associated analogies will be discussed.Comment: 18 pages; 6 figures, 2 tables, 4 supplemental figure

Induction of pathogenic Th17 cells by inducible salt sensing kinase SGK1

Th17 cells are highly proinflammatory cells critical for clearing extracellular pathogens and for induction of multiple autoimmune diseases1. IL-23 plays a critical role in stabilizing and reinforcing the Th17 phenotype by increasing expression of IL-23 receptor (IL-23R) and endowing Th17 cells with pathogenic effector functions2, 3. However, the precise molecular mechanism by which IL-23 sustains the Th17 response and induces pathogenic effector functions has not been elucidated. Here, we used transcriptional profiling of developing Th17 cells to construct a model of their signaling network and nominate major nodes that regulate Th17 development. We identified serum glucocorticoid kinase-1 (SGK1), a serine-threonine kinase4, as an essential node downstream of IL-23 signaling. SGK1 is critical for regulating IL-23R expression and stabilizing the Th17 cell phenotype by deactivation of Foxo1, a direct repressor of IL-23R expression. SGK1 has been shown to govern Na+ transport and salt (NaCl) homeostasis in other cells5, 6, 7, 8. We here show that a modest increase in salt concentration induces SGK1 expression, promotes IL-23R expression and enhances Th17 cell differentiation in vitro and in vivo, accelerating the development of autoimmunity. Loss of SGK1 abrogated Na+-mediated Th17 differentiation in an IL-23-dependent manner. These data demonstrate that SGK1 plays a critical role in the induction of pathogenic Th17 cells and provides a molecular insight into a mechanism by which an environmental factor such as a high salt diet triggers Th17 development and promotes tissue inflammation

A Growth Curve Model with Fractional Polynomials for Analysing Incomplete Time-Course Data in Microarray Gene Expression Studies

Identifying the various gene expression response patterns is a challenging issue in expression microarray time-course experiments. Due to heterogeneity in the regulatory reaction among thousands of genes tested, it is impossible to manually characterize a parametric form for each of the time-course pattern in a gene by gene manner. We introduce a growth curve model with fractional polynomials to automatically capture the various time-dependent expression patterns and meanwhile efficiently handle missing values due to incomplete observations. For each gene, our procedure compares the performances among fractional polynomial models with power terms from a set of fixed values that offer a wide range of curve shapes and suggests a best fitting model. After a limited simulation study, the model has been applied to our human in vivo irritated epidermis data with missing observations to investigate time-dependent transcriptional responses to a chemical irritant. Our method was able to identify the various nonlinear time-course expression trajectories. The integration of growth curves with fractional polynomials provides a flexible way to model different time-course patterns together with model selection and significant gene identification strategies that can be applied in microarray-based time-course gene expression experiments with missing observations

Interplay of Dynamic Transcription and Chromatin Remodeling: Lessons from Yeast

Regulation of transcription involves dynamic rearrangements of chromatin structure. The budding yeast Saccharomyces cerevisiae has a variety of highly conserved factors necessary for these reconstructions. Chromatin remodelers, histone modifiers and histone chaperones directly associate to promoters and open reading frames of exposed genes and facilitate activation and repression of transcription. We compare two distinct patterns of induced transcription: Sustained transcribed genes switch to an activated state where they remain as long as the induction signal is present. In contrast, single pulsed transcribed genes show a quick and strong induction pulse resulting in high transcript levels followed by adaptation and repression to basal levels. We discuss intensively studied promoters and coding regions from both groups for their co-factor requirements during transcription. Interplay between chromatin restructuring factors and dynamic transcription is highly variable and locus dependent

Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells

available in PMC 2011 November 01.Cellular RNA levels are determined by the interplay of RNA production, processing and degradation. However, because most studies of RNA regulation do not distinguish the separate contributions of these processes, little is known about how they are temporally integrated. Here we combine metabolic labeling of RNA at high temporal resolution with advanced RNA quantification and computational modeling to estimate RNA transcription and degradation rates during the response of mouse dendritic cells to lipopolysaccharide. We find that changes in transcription rates determine the majority of temporal changes in RNA levels, but that changes in degradation rates are important for shaping sharp 'peaked' responses. We used sequencing of the newly transcribed RNA population to estimate temporally constant RNA processing and degradation rates genome wide. Degradation rates vary significantly between genes and contribute to the observed differences in the dynamic response. Certain transcripts, including those encoding cytokines and transcription factors, mature faster. Our study provides a quantitative approach to study the integrative process of RNA regulation.Human Frontier Science Program (Strasbourg, France)Howard Hughes Medical InstituteBurroughs Wellcome Fund (Career Award at the Scientific Interface

A Generic and Cell-Type-Specific Wound Response Precedes Regeneration in Planarians

Regeneration starts with injury. Yet how injuries affect gene expression in different cell types and how distinct injuries differ in gene expression remain unclear. We defined the transcriptomes of major cell types of planarians—flatworms that regenerate from nearly any injury—and identified 1,214 tissue-specific markers across 13 cell types. RNA sequencing on 619 single cells revealed that wound-induced genes were expressed either in nearly all cell types or specifically in one of three cell types (stem cells, muscle, or epidermis). Time course experiments following different injuries indicated that a generic wound response is activated with any injury regardless of the regenerative outcome. Only one gene, notum, was differentially expressed early between anterior- and posterior-facing wounds. Injury-specific transcriptional responses emerged 30 hr after injury, involving context-dependent patterning and stem-cell-specialization genes. The regenerative requirement of every injury is different; however, our work demonstrates that all injuries start with a common transcriptional response.Broad Institute of MIT and Harvard. Klarman Cell ObservatoryHoward Hughes Medical Institute (Investigator)National Institutes of Health (U.S.) (NIH grant R01GM080639)European Molecular Biology Organization (Fellowship)National Institutes of Health (U.S.) (NIH grant F32 HD075541

Nat Biotechnol

Cellular RNA levels are determined by the interplay of RNA production, processing and degradation. However, because most studies of RNA regulation do not distinguish the separate contributions of these processes, little is known about how they are temporally integrated. Here we combine metabolic labeling of RNA at high temporal resolution with advanced RNA quantification and computational modeling to estimate RNA transcription and degradation rates during the response of mouse dendritic cells to lipopolysaccharide. We find that changes in transcription rates determine the majority of temporal changes in RNA levels, but that changes in degradation rates are important for shaping sharp 'peaked' responses. We used sequencing of the newly transcribed RNA population to estimate temporally constant RNA processing and degradation rates genome wide. Degradation rates vary significantly between genes and contribute to the observed differences in the dynamic response. Certain transcripts, including those encoding cytokines and transcription factors, mature faster. Our study provides a quantitative approach to study the integrative process of RNA regulation.DP1 OD003958/OD/NIH HHS/United StatesDP1 OD003958-04/OD/NIH HHS/United StatesDP1-00003958-01/DP/NCCDPHP CDC HHS/United StatesDP2 OD002230/OD/NIH HHS/United StatesS10 RR026688/RR/NCRR NIH HHS/United StatesU54 AI057159/AI/NIAID NIH HHS/United StatesHoward Hughes Medical Institute/United StatesHoward Hughes Medical Institute/United States2011-11-01T00:00:00Z21516085PMC311463

The NIP7 protein is required for accurate pre-rRNA processing in human cells

Eukaryotic ribosome biogenesis requires the function of a large number of trans-acting factors which interact transiently with the nascent pre-rRNA and dissociate as the ribosomal subunits proceed to maturation and export to the cytoplasm. Loss-of-function mutations in human trans-acting factors or ribosome components may lead to genetic syndromes. In a previous study, we have shown association between the SBDS (Shwachman–Bodian–Diamond syndrome) and NIP7 proteins and that downregulation of SBDS in HEK293 affects gene expression at the transcriptional and translational levels. In this study, we show that downregulation of NIP7 affects pre-rRNA processing, causing an imbalance of the 40S/60S subunit ratio. We also identified defects at the pre-rRNA processing level with a decrease of the 34S pre-rRNA concentration and an increase of the 26S and 21S pre-rRNA concentrations, indicating that processing at site 2 is particularly slower in NIP7-depleted cells and showing that NIP7 is required for maturation of the 18S rRNA. The NIP7 protein is restricted to the nuclear compartment and co-sediments with complexes with molecular masses in the range of 40S–80S, suggesting an association to nucleolar pre-ribosomal particles. Downregulation of NIP7 affects cell proliferation, consistently with an important role for NIP7 in rRNA biosynthesis in human cells

The NIP7 protein is required for accurate pre-rRNA processing in human cells

Eukaryotic ribosome biogenesis requires the function of a large number of trans-acting factors which interact transiently with the nascent pre-rRNA and dissociate as the ribosomal subunits proceed to maturation and export to the cytoplasm. Loss-of-function mutations in human trans-acting factors or ribosome components may lead to genetic syndromes. In a previous study, we have shown association between the SBDS (Shwachman–Bodian–Diamond syndrome) and NIP7 proteins and that downregulation of SBDS in HEK293 affects gene expression at the transcriptional and translational levels. In this study, we show that downregulation of NIP7 affects pre-rRNA processing, causing an imbalance of the 40S/60S subunit ratio. We also identified defects at the pre-rRNA processing level with a decrease of the 34S pre-rRNA concentration and an increase of the 26S and 21S pre-rRNA concentrations, indicating that processing at site 2 is particularly slower in NIP7-depleted cells and showing that NIP7 is required for maturation of the 18S rRNA. The NIP7 protein is restricted to the nuclear compartment and co-sediments with complexes with molecular masses in the range of 40S–80S, suggesting an association to nucleolar pre-ribosomal particles. Downregulation of NIP7 affects cell proliferation, consistently with an important role for NIP7 in rRNA biosynthesis in human cells

An IL-27-Driven Transcriptional Network Identifies Regulators of IL-10 Expression across T Helper Cell Subsets.

Interleukin-27 (IL-27) is an immunoregulatory cytokine that suppresses inflammation through multiple mechanisms, including induction of IL-10, but the transcriptional network mediating its diverse functions remains unclear. Combining temporal RNA profiling with computational algorithms, we predict 79 transcription factors induced by IL-27 in T cells. We validate 11 known and discover 5 positive (Cebpb, Fosl2, Tbx21, Hlx, and Atf3) and 2 negative (Irf9 and Irf8) Il10 regulators, generating an experimentally refined regulatory network for Il10. We report two central regulators, Prdm1 and Maf, that cooperatively drive the expression of signature genes induced by IL-27 in type 1 regulatory T cells, mediate IL-10 expression in all T helper cells, and determine the regulatory phenotype of colonic Foxp3 <sup>+</sup> regulatory T cells. Prdm1/Maf double-knockout mice develop spontaneous colitis, phenocopying ll10-deficient mice. Our work provides insights into IL-27-driven transcriptional networks and identifies two shared Il10 regulators that orchestrate immunoregulatory programs across T helper cell subsets