46 research outputs found
Traité de diagnostic médical, ou Guide clinique pour l'étude des signes caractéristiques des maladies.
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Mathematical models and computational methods for the analysis of genome-scale protein synthesis
No full textProteins are a ubiquitous and indispensable element for every living organism, from simple bacteria to mammals. Already in the simplest organisms, there exist some thousands of different protein species that take up a great variety of structures, and thus different roles, letting them precisely orchestrate the functioning of each cell. Despite this diversity of functions and shapes, all proteins are emerging from a same root: the DNA that encodes all proteins, in a same way as a dictionary contains the definition of each word. When a cell needs a specific protein, it will therefore "read" this "DNA dictionary" and translate it into another "language": from a nucleotide sequence of the DNA to an amino acids sequence, which is the basis of each protein. This process of "reading" the DNA to form a protein, or in better terms the protein synthesis, lies at the heart of every organism. Indeed 80% of the cellular energy is devoted to protein synthesis. The main mechanisms of this process are the same for all proteins and for all kingdoms of life. A good understanding of this process is therefore essential to biology; any malfunctioning could potentially lead to diseases and, on the other hand, any of the steps of protein synthesis could be a prospective drug target. This is already the case of various antibiotics. In addition to that, a good understanding of this system is also valuable in recombinant vaccine and recombinant drug production, in order to help improve the yield of these proteins. Recombinant proteins technology is used for example to synthesize the hepatitis B vaccine in yeast cells or to synthesize the recombinant human insulin in Escherichia coli cells. This is done by inserting into the organism a DNA plasmid that encodes the given protein so that the transfected organism will then synthesize this protein nearly as if it was from its own DNA. A further benefit from an in -depth knowledge of protein synthesis relates to circuit design in synthetic biology. There, the goal is to design cells that will respond to their environment in a predefined manner, and again, this is done by inserting specific genes into the cells. Understanding protein synthesis can help to estimate the sensibility of such a system as well as help to define characteristics of its response. Nowadays, the many facets of protein synthesis and its regulation are getting increasingly better understood. Nevertheless, it also becomes increasingly more evident that the classical approach of studying every component in isolation should be left aside and the system or cell should be studied as a whole, due to the interconnections of all of its elements: we have entered in the systems biology era. With the recent advances in genomics, transcriptomics, proteomics and other –omics technologies, we are able to measure the state of cells under different conditions in a high -throughput manner, enabling some global, genome -scale view as aimed at by systems biology. The huge amount of data collected by these high -throughput techniques poses a new challenge: how can we efficiently integrate these data to make some sense out of them for gaining deeper understanding and for the design and optimization of novel systems. A general answer is that computational approaches are needed. A model can be built to represent the system, and its outputs can then be compared to the experimental measurements. The great advantage of the modeling and simulation approach is that we can build many different in silico systems to test and to compare which one best represents our current knowledge. This in silico system can then be subjected to different "virtual" conditions, with the goal of observing how the system would behave in response to these conditions, which can be repeated for many cases and conditions in a very cost - and time -effective way in comparison to an in vitro or in vivo experiment. In this thesis, we aim at integrating such high -throughput data into a model for a better understanding of protein synthesis. We mainly focus on the often -neglected steps of translation, to observe their possible influence and regulation on the system. For this, a model incorporating all the steps of translation is built, including the various intermediate translation elongation steps. We then develop a novel, efficient, exact stochastic algorithm, targeted here to simulate translation at the genome‐scale, accounting for the competition between mRNAs for shared resources. This algorithm could easily be adapted to other systems than translation as well. Another novelty is further introduced with a methodology to analyze and estimate polysome sizes from experimental measurements in prokaryotes. Integrating various experimental measurements into our model of translation, we additionally estimate translation characteristics at the genome-scale for prokaryotic and eukaryotic cells, and we observe how the system has been optimized to cope with the cellular needs. We further estimate the sensitivity of protein synthesis on different perturbations like changes in initiation, elongation, termination rates, changes in ribosome availabilities or mRNA copy numbers, or changes following starvation conditions. Taken together, the results from this thesis show that the regulation at the translation steps is stronger than is commonly assumed and it can have many implications on the system.LCS
Financial Restraints in the South Korean Miracle
No full textWe provide novel empirical evidence on the effects of financial restraints on South Korean financial development. The evidence is linked to a simple model of the Korean banking system that encapsulates its cartelised nature, which predicts a positive association between financial development and (i) the degree of state control over the banking system, (ii) mild repression of lending rates. The model also predicts that in the presence of lending rate controls, increases in the level of the administered deposit rate are unlikely to influence financial deepening. We test the model empirically by constructing individual and summary measures of financial restraints. Our empirical findings are consistent with our theoretical predictions but contrast sharply with the predictions of earlier literature that postulates that interest rate ceilings and other financial restraints constitute sources of ‘financial repression’.Financial deepening; financial restraints
Inflammatory B cells correlate with failure to checkpoint blockade in melanoma patients.
No full textThe understanding of the role of B cells in patients with solid tumors remains insufficient. We found that circulating B cells produced TNFα and/or IL-6, associated with unresponsiveness and poor overall survival of melanoma patients treated with anti-CTLA4 antibody. Transcriptome analysis of B cells from melanoma metastases showed enriched expression of inflammatory response genes. Publicly available single B cell data from the tumor microenvironment revealed a negative correlation between TNFα expression and response to immune checkpoint blockade. These findings suggest that B cells contribute to tumor growth via the production of inflammatory cytokines. Possibly, these B cells are different from tertiary lymphoid structure-associated B cells, which have been described to correlate with favorable clinical outcome of cancer patients. Further studies are required to identify and characterize B cell subsets and their functions promoting or counteracting tumor growth, with the aim to identify biomarkers and novel treatment targets
Author response: Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data
No full textRaw and normalized (H/M)<sub>K</sub>, (H/M)<sub>R</sub> ratios measured in pcSILAC experiments.
No full text<p><b>A</b>),<b>B</b>) Scatter plots of global ratios of pre-existing protein ((H/M)<b><sub>R</sub></b>) and newly synthesized protein ((H/M)<b><sub>K</sub></b>) after correction for mixing ratio and normalization around the median. Values shown are for pcSILAC experiment 1 (average of two replicates) at t = 6h (A) and t = 20h (B). Reference (red) and other proteins discussed in the text (blue) are indicated. <b>C</b>) Evolution in time after start of GA treatment of normalized ratios of total protein (stSILAC, blue), newly synthesized proteins ((H/M)<sub>K</sub>, green) and pre-existing protein ((H/M)<sub>R</sub>, red). Data points refer to values at t = 6,12,20h after addition of GA (t = 0). pcSILAC ratios were normalized (i.e. centered around population median) to facilitate comparison with changes in total protein levels. Fitting of the values for DNAJB1 to the model was very poor due to its complex behavior, therefore decay and synthesis rates could not be calculated for this early induced protein. <b>D</b>) Box plots (experiment 1) of global log<sub>2</sub>(H/M)<b><sub>K</sub></b> and log<sub>2</sub>(H/M)<b><sub>R</sub></b> ratios after mixing ratio correction but before normalization <b>E</b>) same as D), but values are shown after correction for mixing ratio and normalization.</p
Results from calculations of decay constants, synthesis rates and evolution of total protein levels.
No full text<p>Indexes of variables are A (as in k<sub>A</sub>, V<sub>A</sub>) for control (+DMSO) and B (as in k<sub>B</sub>, V<sub>B</sub>) for treated (+GA) cells. <b>A</b>) Scatter plot of the values of the degradation constants for the control and treated sample (experiment 2, 911 proteins). The position of reference proteins is indicated. The dashed line indicates a 1:1 relationship <b>B</b>) Scatter plot of V<sub>A</sub> and V<sub>B</sub> (same dataset as A). Other heat shock proteins are shown in pink. The dashed line indicates a 1:1 relationship <b>C</b>) Kernel density estimate of log<sub>2</sub> of ratios of synthesis rates (V<sub>B</sub>/V<sub>A</sub>) and degradation constants (k<sub>B,d</sub>/k<sub>A,d</sub>) after correction for cell growth. <b>D</b>) Comparison of distributions of log<sub>2</sub> of ratios <i>S</i> of net protein levels (treated/control) calculated from pcSILAC data at t = 6, 12, 20h vs. ratios at steady-state (t = infinite) calculated from the model. <i>S</i> values were corrected for mixing inequalities.</p
Information et éducation des familles des enfants en situation d’urgence : méthodes d’élaboration des messages
No full textDynamic changes in mRNA levels and net protein abundances (stSILAC), protein synthesis, or decay (pcSILAC) rates for a selection of transcripts/proteins from several clusters upon Hsp90 inhibition.
No full text<p>Plots show data derived from stSILAC (A and B) and from pcSILAC experiment 2 (C, D, E, and F). <b>A</b>) Variations in mRNA (log<sub>2</sub> fold-change) versus protein levels at 5-6h. <b>B</b>) Variations in mRNA (log<sub>2</sub> fold-change) versus protein levels at 19-20h. <b>C</b>) Plot describing the variations in mRNA (log<sub>2</sub> fold-change) at 5h versus protein synthesis rates (log<sub>2</sub> [V<sub>s_GA</sub>/V<sub>s_DMSO</sub>]). <b>D</b>) same as C) but mRNA at t = 19h. <b>E</b>) Plots describing the variations in mRNA (log<sub>2</sub> fold-change) at 5h versus decay rates (log<sub>2</sub> [k<sub>s_GA</sub>/k<sub>s_DMSO</sub>]). <b>F</b>) same as E) but at t = 19-20h. A selection of transcripts encoding Hsp90, cofactor DNAJB1, Hsp90 clients Cdk6, Lck, FASN and the novel potential Hsp90 client ITK, are labelled.</p
Changes in degradation and synthesis rates induced by Hsp90 inhibition and relationship with changes in net total protein levels and protein families.
No full text<p>Indexes of variables are A (as in k<sub>A</sub>, V<sub>A</sub>) for control (+DMSO) and B (as in k<sub>B</sub>, V<sub>B</sub>) for treated (+GA) cells. A) Scatter plot of the values of ratios of intrinsic degradation constants k<sub>B,d</sub>/k<sub>A,d</sub> vs. the ratios of synthesis rates V<sub>B</sub>/V<sub>A.</sub> The median values of k<sub>B,d</sub>/k<sub>A,d</sub> and V<sub>B</sub>/V<sub>A</sub> for the population are indicated with dashed lines. Coloring of points is according to the calculated treated /control total protein ratio at t = 20h (ratio <i>S</i>). B) Same as A) but with coloring of ribosomal, proteasome and heat shock proteins. Groups I and II are discussed in the main text. All values are log<sub>2</sub>.</p
