7,165 research outputs found
Optimal decoding of information from a genetic network
Gene expression levels carry information about signals that have functional
significance for the organism. Using the gap gene network in the fruit fly
embryo as an example, we show how this information can be decoded, building a
dictionary that translates expression levels into a map of implied positions.
The optimal decoder makes use of graded variations in absolute expression
level, resulting in positional estimates that are precise to ~1% of the
embryo's length. We test this optimal decoder by analyzing gap gene expression
in embryos lacking some of the primary maternal inputs to the network. The
resulting maps are distorted, and these distortions predict, with no free
parameters, the positions of expression stripes for the pair-rule genes in the
mutant embryos
When it Pays to Rush: Interpreting Morphogen Gradients Prior to Steady-State
During development, morphogen gradients precisely determine the position of
gene expression boundaries despite the inevitable presence of fluctuations.
Recent experiments suggest that some morphogen gradients may be interpreted
prior to reaching steady-state. Theoretical work has predicted that such
systems will be more robust to embryo-to-embryo fluctuations. By analysing two
experimentally motivated models of morphogen gradient formation, we investigate
the positional precision of gene expression boundaries determined by
pre-steady-state morphogen gradients in the presence of embryo-to-embryo
fluctuations, internal biochemical noise and variations in the timing of
morphogen measurement. Morphogens that are direct transcription factors are
found to be particularly sensitive to internal noise when interpreted prior to
steady-state, disadvantaging early measurement, even in the presence of large
embryo-to-embryo fluctuations. Morphogens interpreted by cell-surface receptors
can be measured prior to steady-state without significant decrease in
positional precision provided fluctuations in the timing of measurement are
small. Applying our results to experiment, we predict that Bicoid, a
transcription factor morphogen in Drosophila, is unlikely to be interpreted
prior to reaching steady-state. We also predict that Activin in Xenopus and
Nodal in zebrafish, morphogens interpreted by cell-surface receptors, can be
decoded in pre-steady-state.Comment: 18 pages, 3 figure
A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer.
Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called "digital biopsy," that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non-small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of <3 minutes per nodule. We simulated additional digital biopsies using morphological procedures. Finally, we compared the features extracted from these digital biopsies with our reference standard using intraclass correlation coefficient (ICC) to characterize robustness. Comparing the reference standard segmentations to our digital biopsies, we found that 84/94 features had an ICC >0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required
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A single H/ACA small nucleolar RNA mediates tumor suppression downstream of oncogenic RAS.
Small nucleolar RNAs (snoRNAs) are a diverse group of non-coding RNAs that direct chemical modifications at specific residues on other RNA molecules, primarily on ribosomal RNA (rRNA). SnoRNAs are altered in several cancers; however, their role in cell homeostasis as well as in cellular transformation remains poorly explored. Here, we show that specific subsets of snoRNAs are differentially regulated during the earliest cellular response to oncogenic RASG12V expression. We describe a novel function for one H/ACA snoRNA, SNORA24, which guides two pseudouridine modifications within the small ribosomal subunit, in RAS-induced senescence in vivo. We find that in mouse models, loss of Snora24 cooperates with RASG12V to promote the development of liver cancer that closely resembles human steatohepatitic hepatocellular carcinoma (HCC). From a clinical perspective, we further show that human HCCs with low SNORA24 expression display increased lipid content and are associated with poor patient survival. We next asked whether ribosomes lacking SNORA24-guided pseudouridine modifications on 18S rRNA have alterations in their biophysical properties. Single-molecule Fluorescence Resonance Energy Transfer (FRET) analyses revealed that these ribosomes exhibit perturbations in aminoacyl-transfer RNA (aa-tRNA) selection and altered pre-translocation ribosome complex dynamics. Furthermore, we find that HCC cells lacking SNORA24-guided pseudouridine modifications have increased translational miscoding and stop codon readthrough frequencies. These findings highlight a role for specific snoRNAs in safeguarding against oncogenic insult and demonstrate a functional link between H/ACA snoRNAs regulated by RAS and the biophysical properties of ribosomes in cancer
Two-photon imaging and analysis of neural network dynamics
The glow of a starry night sky, the smell of a freshly brewed cup of coffee
or the sound of ocean waves breaking on the beach are representations of the
physical world that have been created by the dynamic interactions of thousands
of neurons in our brains. How the brain mediates perceptions, creates thoughts,
stores memories and initiates actions remains one of the most profound puzzles
in biology, if not all of science. A key to a mechanistic understanding of how
the nervous system works is the ability to analyze the dynamics of neuronal
networks in the living organism in the context of sensory stimulation and
behaviour. Dynamic brain properties have been fairly well characterized on the
microscopic level of individual neurons and on the macroscopic level of whole
brain areas largely with the help of various electrophysiological techniques.
However, our understanding of the mesoscopic level comprising local populations
of hundreds to thousands of neurons (so called 'microcircuits') remains
comparably poor. In large parts, this has been due to the technical
difficulties involved in recording from large networks of neurons with
single-cell spatial resolution and near- millisecond temporal resolution in the
brain of living animals. In recent years, two-photon microscopy has emerged as
a technique which meets many of these requirements and thus has become the
method of choice for the interrogation of local neural circuits. Here, we
review the state-of-research in the field of two-photon imaging of neuronal
populations, covering the topics of microscope technology, suitable fluorescent
indicator dyes, staining techniques, and in particular analysis techniques for
extracting relevant information from the fluorescence data. We expect that
functional analysis of neural networks using two-photon imaging will help to
decipher fundamental operational principles of neural microcircuits.Comment: 36 pages, 4 figures, accepted for publication in Reports on Progress
in Physic
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