231,167 research outputs found
A single E-box in the <i>Cel-lin-3</i> CRM is not sufficient for <i>lin-3</i> expression in the anchor cell of <i>C</i>. <i>elegans</i>.
No full text(A) New cis-regulatory lin-3 alleles with deleted E-boxL and NHR or NHR and E-boxR. (B) Quantification of vulval induction in these new mutants. Note the complete absence of any induction in the recovered lin-3 alleles (n>30). Scorings of lin-3(1417) animals are the same as those reported in Fig 5 and are used here to indicate that this mutation leads to vulval hypo-induction rather than no induction at all. (C-D) smFISH in lin-3(mf72) (C) and N2 (D) animals. Green spots correspond to lin-3 transcripts and red spots to lag-2 that is used as an anchor cell marker. Blue is DAPI staining of nuclei. Note the absence of lin-3 expression in the anchor cell in the lin-3(mf72) mutant animal. Absence of lin-3 signal in the anchor cell was also confirmed for the other lin-3 alleles.</p
The stem cell E3-ligase Lin-41 promotes liver cancer progression through inhibition of microRNA-mediated gene silencing
No full textLin-41 is a stem cell-specific E3 ligase and a known target of the tumour suppressor microRNA (miRNA) let-7. Lin-41 was recently reported to mediate ubiquitylation and degradation of the miRNA pathway protein Ago2. We demonstrate that Lin-41 is over-expressed in hepatocellular carcinoma (HCC). Lin-41 over-expression correlates with high a-fetoprotein level, high tumour grade and high tumour stage and predicts early tumour recurrence. Lin-41 is a strong predictor of poor long-term survival for patients with HCC. Lin-41 knock-down by RNA interference in HCC cell lines Huh7 and Hep3B suppressed proliferation in vitro and reduced in vivo tumour growth in NOD/SCID mice. On the other hand, over-expression of Lin-41 in the HCC cell line SK-Hep1 enhanced tumourigenicity. Over-expression and knock-down of Lin-41 led to inverse changes in the levels of Ago1 and Ago2 proteins. Over-expression of Ago1 and Ago2 reduced in vivo tumour growth. Lin-41 over-expression suppressed let-7 activity in HCC cell lines and expression of Lin-41 enhanced the expression of let-7-regulated oncogenes c-Myc, Lin-28B, HMGA2 and type 1 insulin-like growth factor receptor (IGF1R). Expression of Lin-28B and c-Myc enhanced the expression of Lin-41. Chromatin immunoprecipitation and reporter assays revealed direct association of c-Myc with the Lin-41 promoter, resulting in transcriptional transactivation. Our results indicate that Lin-41 plays an important role in the growth of HCC by regulating RISC complex proteins Ago1 and Ago2 to inhibit miRNA-mediated gene silencing and promote the expression of oncogenic proteins. Lin-41 is also a strong prognostic factor for patients with HCC. Copyright (C) 2012 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd
On the sheaf-theoretic SL(2, C) Casson–Lin invariant
No full textWe prove that the (τ-weighted, sheaf-theoretic) SL(2, C) Casson–Lin invariant introduced by Manolescu and the first author is generically independent of the parameter τ and additive under connected sums of knots in integral homology 3-spheres. This addresses two questions asked by Manolescu and the first author. Our arguments involve a mix of topology and algebraic geometry, and rely crucially on the fact that the SL(2, C) Casson–Lin invariant admits an alternative interpretation via the theory of Behrend functions.</p
Spatial Chow-Lin Methods for Data Completion in Econometric Flow Models
Full text linkFlow data across regions can be modeled by spatial econometric models, see LeSage and Pace (2009). Recently, regional studies became interested in the aggregation and disaggregation of flow models, because trade data cannot be obtained at a disaggregated level but data are published on an aggregate level. Furthermore, missing data in disaggregated flow models occur quite often since detailed measurements are often not possible at all observation points in time and space. In this paper we develop classical and Bayesian methods to complete flow data. The Chow and Lin (1971) method was developed for completing disaggregated incomplete time series data. We will extend this method in a general framework to spatially correlated flow data using the cross-sectional Chow-Lin method of Polasek et al. (2009). The missing disaggregated data can be obtained either by feasible GLS prediction or by a Bayesian (posterior) predictive density.Missing values in spatial econometrics, MCMC, non-spatial Chow-Lin (CL) and spatial Chow-Lin (SCL) methods, spatial internal flow (SIF) models, origin and destination (OD) data
Quantitative Insights into Developmental Signals and Phenotypes in C. elegans
Full text linkDesign of biomaterials and cellular scaffolds for tissue-engineering applications and regenerative medicine requires a precise understanding of the principles underlying multicellular patterning. Adhesion, migration, division, differentiation, and apoptosis are characteristic cellular behaviors, the engineering of which has the potential to allow creation of custom, multicellular structures. These cellular events occur naturally during embryonic and postembryonic development of multicellular organisms. Development thus offers the opportunity to learn about the design principles and molecular mechanisms that guide cellular patterning.
A key finding in developmental biology is that a limited set of conserved molecular signaling pathways act at multiple times and locations throughout the embryo to introduce cell-fate asymmetries in homogenous populations of cells. In turn, these asymmetries serve as starting points for the patterning of new organs. These signaling pathways interact quantitatively at multiple levels, including signaling cues, post-translational regulation, and gene-regulatory networks, to guide multicellular patterning.
How does the quantitative performance of these signaling networks ensure the intended phenotype pattern? How do changes in the quantitative performance of these networks, possibly over the course of evolution, give rise to new phenotypes? These are the central questions pursued in this thesis.
In order to answer such questions, we used vulva formation in the nematode Caenorhabditis elegans as a model system of cellular patterning. We formulated a mathematical model of the molecular network underlying cellular-fate specification in this system. Computational analysis of this molecular network reveals that cell–cell coupling through lateral LIN-12/Notch signaling amplifies the perception of the gradient in the epidermal-growth-factor-like soluble cue, LIN-3. Thus, the gradient in LIN-3 concentration produces an even steeper difference in LIN-3-mediated intracellular signals between adjoining cells. Such gradient amplification may be particularly important in converting a shallow, graded-specification signal into a spatial pattern of distinct fate choices.
Through quantitative perturbations of interaction strengths between components of the vulval patterning network, we further show that our modeling approach can correctly predict phenotype patterns observed in C. elegans mutation studies. This study generated a framework for quantitative analysis of molecular networks that links quantitative molecular perturbations to patterning outcomes. This framework will prove useful in the analysis of other systems involving cellular fate decisions and in tissue engineering applications where the generation of precise cell patterns is needed. We demonstrate the generality of our approach through an application to evolutionary developmental biology. Since molecular connectivity of the vulva patterning network of several closely related Caenorhabditis species is preserved, we correctly predict the quantitative diversification that must have occurred in this network during species evolution.</p
LIN-1 sumoylation is required for ventral toroid contraction.
No full text(A) Wild-type and K10A, K169A mutant LIN-1::GFP expression in L3 larvae at the Pn.px stage after VPC-specific degradation of AID::SMO-1 from the L2 stage onward. The 1° and 2° VPC descendants are underlined in white. The left panels show the corresponding DIC images overlaid with the LIN-1::GFP signal in green. (B) Quantification of LIN-1::GFP expression levels in 1° and 2° VPC descendants at the Pn.px stage in LIN-1::GFP wild-type and K10A, K169A double mutants under the indicated conditions. See S3 Fig for the corresponding measurements at the Pn.pxx stage. (C) Toroid morphogenesis defects in LIN-1 K10A and K169A single and double mutants at the L4 stage. Left panels show lateral views of z-projections. vulA and vulB1 toroids are outlined by the white rectangle in the top left panel and shown in top (xz) views in the right panels. (D) Quantification of vulA contraction, calculated as the ratio of the vulA and vulB1 toroid diameter. The box plots show the median values with the 25th and 75th percentiles and the whiskers indicate the maximum and minimum values. Where indicated, untreated controls are labelled with–IAA (blue) and animals treated with 1 mM auxin with +IAA (red). In each graph, the numbers of animals scored are indicated by the numbers in brackets. Statistical significance in (B) and (D) was calculated with unpaired two-tailed t-tests. p-values are indicated as * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001. The scale bars are 10 μm.</p
LIN-39 promotes neuronal fate specification in the Q and V5 lineage.
No full text(A) The expression of lin-39 in AVM, SDQL/R, PDEL/R, and PVDL/R, indicated by the overlapping with neurotransmitter identity markers and specific fate markers (uIs115[mec-17p::TagRFP] for AVM, otIs181[dat-1p::mCh] for PDE, uIs117[lad-2p::mCh] for SDQ). (B) The expression of mab-5 in SDQL. (C) Summary of lin-39 (green) and mab-5 (cyan) expression in the descendants of Q and V5 lineages. (D) The loss of gcy-37 expression in AQR and AVM neurons in lin-39(n1760) mutants and the mispositioning of PQR in mab-5(gk670) mutants; the loss of lad-2 expression in SDQR in lin-39(n1760) mutants, the displacement of SDQL in mab-5(gk670) mutants, and the loss of lad-2 expression in both SDQs in lin-39(n1760) mab-5(e1239) mutants. The right panels show the penetrance for the loss of marker expression and cell body mispositioning. Mean ± SD for the percentage of cells showing corresponding phenotypes from three biological replicates are shown. Double asterisks indicate statistically significant difference (p Chi-square test. (E) The loss of ser-2 expression in PVD and PDE neurons and the loss of F49H12.4 expression in PVD in lin-39(n1760) and ceh-20(u843) mutants. (F) Dopaminergic marker dat-1 is normally expressed in PDE neurons in lin-39 mutants, but PDE shows axonal growth defects. The arrows indicate the termini of PDE axons. The expression of glutamatergic identity marker eat-4 and the PVD terminal selector gene mec-3 in PVD neurons in lin-39 mutants.</p
SPATIAL CHOW-LIN METHODS: BAYESIAN AND ML FORECAST COMPARISONS
Full text linkCompleting data that are collected in disaggregated and heterogeneous spatial units is a quite frequent problem in spatial analyses of regional data. Chow and Lin (1971) (CL) were the rst to develop a uni ed framework for the three problems (interpolation, extrapolation and distribution) of predicting disaggregated times series by so-called indicator series. This paper develops a spatial CL procedure for disaggregating cross-sectional spatial data and compares the Maximum Likelihood and Bayesian spatial CL forecasts with the naive pro rata error distribution. We outline the error covariance structure in a spatial context, derive the BLUE for the ML estimator and the Bayesian estimation procedure by MCMC. Finally we
apply the procedure to European regional GDP data and discuss the disaggregation assumptions. For the evaluation of the spatial Chow-Lin procedure we assume that only NUTS 1 GDP is known and predict it at NUTS 2 by using employment and spatial information available at NUTS 2. The spatial neighborhood is de ned by the inverse travel time by car in minutes. Finally, we present the forecast accuracy criteria comparing the predicted values with the actual observations.
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