Distinguishing Modes of Eukaryotic Gradient Sensing

The behaviors of biological systems depend on complex networks of interactions between large numbers of components. The network of interactions that allows biological cells to detect and respond to external gradients of small molecules with directed movement is an example where many of the relevant components have been identified. This behavior, called chemotaxis, is essential for biological functions ranging from immune response in higher animals to the food gathering and social behavior of ameboid cells. Gradient sensing is the component of this behavior whereby cells transduce the spatio-temporal information in the external stimulus into an internal distribution of molecules that mediate the mechanical and morphological changes necessary for movement. Signaling by membrane lipids, in particular 3' phosphoinositides (3'PIs), is thought to play an important role in this transduction. Key features of the network of interactions that regulates the dynamics of these lipids are coupled positive feedbacks that might lead to response bifurcations and the involvement of molecules that translocate from the cytosol to the membrane, coupling responses at distant point on the cell surface. Both are likely to play important roles in amplifying cellular responses and shaping their qualitative features. To better understand the network of interactions that regulates 3'PI dynamics in gradient sensing, we develop a computational model at an intermediate level of detail. To investigate how the qualitative features of cellular response depend on the structure of this network, we define four variants of our model by adjusting the effectiveness of the included feedback loops and the importance of translocating molecules in response amplification. Simulations of characteristic responses suggest that differences between our model variants are most evident at transitions between efficient gradient detection and failure. Based on these results, we propose criteria to distinguish between possible modes of gradient sensing in real cells, where many biochemical parameters may be unknown. We also identify constraints on parameters required for efficient gradient detection. Finally, our analysis suggests how a cell might transition between responsiveness and non-responsiveness, and between different modes of gradient sensing, by adjusting its biochemical parameters

Ekstrak Bawang Putih Bubuk Dengan Menggunakan Proses Spray Drying

Bawang putih banyak digunakan sebagai bumbu utama pada berbagai masakan karena aromanya yang khas. Aroma khas tersebut karena adanya komponen aktif (Allicin) pada bawang putih. Allicin juga berguna untuk daya anti kolesterol yang dapat mencegah penyakit jantung, tekanan darah tinggi dan lain sebagainya. Komponen Allicin bersifat volatil sehingga bila penanganannya salah maka dapat menyebabkan kerusakan. Untuk mengawetkan bawang putih yaitu dengan cara pengeringan. Salah satu proses yang dapat digunakan adalah spray drying karena proses ini membutuhkan waktu yang singkat. Proses spray drying adalah proses pengeringan dengan cara menyemprotkan fase cair menjadi butiran-butiran kecil kemudian mengontakkannya dengan udara panas sehingga menjadi bubuk. Umpan yang akan dikeringkan dapat berupa larutan ataupun suspensi dengan viskositas tertentu. Penelitian ini dilakukan percobaan pembuatan ekstrak bawang putih bubuk dengan variasi perbandingan massa bawang putih dengan pelarut air tertentu yang dimulai dari perbandingan 1:1, variasi konsentrasi maltodekstrin 0%, 10%, 20%, 30%, 40% dan 50%, serta variasi suhu udara masuk 160 oC, 170 oC, 180 oC dan 190 oC. Hal yang diamati adalah pengaruh konsentrasi maltodekstrin dan suhu udara masuk terhadap karakteristik ekstrak bawang putih bubuk yang dihasilkan. Karakteristik bubuk yang dianalisa meliputi kadar air, bulk density, wettability, solubility dan organoleptik. Dari hasil analisa diketahui bahwa dengan meningkatnya suhu udara inlet menyebabkan terjadinya penurunan kadar air. Begitu juga dengan meningkatnya suhu udara masuk menyebabkan terjadinya peningkatan bulk density, wettability dan solubility

HIV Promoter Integration Site Primarily Modulates Transcriptional Burst Size Rather Than Frequency

Mammalian gene expression patterns, and their variability across populations of cells, are regulated by factors specific to each gene in concert with its surrounding cellular and genomic environment. Lentiviruses such as HIV integrate their genomes into semi-random genomic locations in the cells they infect, and the resulting viral gene expression provides a natural system to dissect the contributions of genomic environment to transcriptional regulation. Previously, we showed that expression heterogeneity and its modulation by specific host factors at HIV integration sites are key determinants of infected-cell fate and a possible source of latent infections. Here, we assess the integration context dependence of expression heterogeneity from diverse single integrations of a HIV-promoter/GFP-reporter cassette in Jurkat T-cells. Systematically fitting a stochastic model of gene expression to our data reveals an underlying transcriptional dynamic, by which multiple transcripts are produced during short, infrequent bursts, that quantitatively accounts for the wide, highly skewed protein expression distributions observed in each of our clonal cell populations. Interestingly, we find that the size of transcriptional bursts is the primary systematic covariate over integration sites, varying from a few to tens of transcripts across integration sites, and correlating well with mean expression. In contrast, burst frequencies are scattered about a typical value of several per cell-division time and demonstrate little correlation with the clonal means. This pattern of modulation generates consistently noisy distributions over the sampled integration positions, with large expression variability relative to the mean maintained even for the most productive integrations, and could contribute to specifying heterogeneous, integration-site-dependent viral production patterns in HIV-infected cells. Genomic environment thus emerges as a significant control parameter for gene expression variation that may contribute to structuring mammalian genomes, as well as be exploited for survival by integrating viruses

HIV promoter integration site primarily modulates transcriptional burst size rather than frequency.

Mammalian gene expression patterns, and their variability across populations of cells, are regulated by factors specific to each gene in concert with its surrounding cellular and genomic environment. Lentiviruses such as HIV integrate their genomes into semi-random genomic locations in the cells they infect, and the resulting viral gene expression provides a natural system to dissect the contributions of genomic environment to transcriptional regulation. Previously, we showed that expression heterogeneity and its modulation by specific host factors at HIV integration sites are key determinants of infected-cell fate and a possible source of latent infections. Here, we assess the integration context dependence of expression heterogeneity from diverse single integrations of a HIV-promoter/GFP-reporter cassette in Jurkat T-cells. Systematically fitting a stochastic model of gene expression to our data reveals an underlying transcriptional dynamic, by which multiple transcripts are produced during short, infrequent bursts, that quantitatively accounts for the wide, highly skewed protein expression distributions observed in each of our clonal cell populations. Interestingly, we find that the size of transcriptional bursts is the primary systematic covariate over integration sites, varying from a few to tens of transcripts across integration sites, and correlating well with mean expression. In contrast, burst frequencies are scattered about a typical value of several per cell-division time and demonstrate little correlation with the clonal means. This pattern of modulation generates consistently noisy distributions over the sampled integration positions, with large expression variability relative to the mean maintained even for the most productive integrations, and could contribute to specifying heterogeneous, integration-site-dependent viral production patterns in HIV-infected cells. Genomic environment thus emerges as a significant control parameter for gene expression variation that may contribute to structuring mammalian genomes, as well as be exploited for survival by integrating viruses

A computational model of LTR transcription with Tat feedback demonstrates noise-driven Switching phenotypes with delayed activation/deactivation (A) Model schematic: The viral LTR promoter probabilistically switches between a transcriptionally inactive state and a transcriptionally active state, with rates and . In the active state, transcripts are produced with rate , and degraded at rate .

<p>Protein translation occurs from each transcript independently at rate , and each protein is degraded with rate . As a model of basal transcription, all rates are assumed constant, and transcript is produced in bursts when and is of order 1 or greater <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003135#pcbi.1003135-Skupsky1" target="_blank">[22]</a>. For the transactivation circuit, the translated protein is Tat (plus GFP), and we include a Michaelis-Menten-like dependence on Tat for the promoter activation and the transcription rates (highlighted in red in the model schematic): , , . The parameters and specify fold-amplification at saturated Tat binding, and specifies the saturation concentration. The model output is the predicted steady-state distribution of protein (GFP and Tat) count across a clonal population of cells, which is then converted to cytometer RFU based on previous calibration <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003135#pcbi.1003135-Skupsky1" target="_blank">[22]</a>. (B) Simulated protein distributions were evolved over time from a Dim initialization (left) for representative parameter values that lead to Dim, Switching, and Bright steady-state phenotypes (right, blue curves). Simulated steady-state basal expression distributions for the same parameter values without Tat feedback are given for comparison (i.e. ; green curves). Simulated histograms are normalized and plotted on the same fluorescence axis as the cytometer data in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003135#pcbi-1003135-g001" target="_blank">Figure 1</a>. (C) A phase diagram summarizes the expression phenotypes predicted by the Tat feedback model as basal transcription parameters ( and ) are varied over the observed experimental range of values while remaining model parameters are fixed. Drawn boundaries separate parameter combinations leading to distinct expression phenotypes. Model-predicted equilibration times (i.e., the time after which half of a Dim-initialized population crosses an intermediate expression threshold between Dim and Bright) are represented on a color scale, with longer times predicted for parameter combinations that specify Switching phenotypes. Parameter combinations used in (B) are marked with an asterisk.</p

Computational models exploring Switching fraction modulation by the Sp1 mutation.

<p>(A) Model phase diagrams varying basal transcriptional parameters at fixed values of Tat feedback parameters. Drawn boundaries separate parameter combinations leading to distinct phenotypes (as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003135#pcbi-1003135-g002" target="_blank">Figure 2C</a>). Superimposed color map estimates the probability density with which the virus samples basal transcription parameters over genomic integrations for the WT promoter (left) and Sp1 mutant promoter (right). Tat feedback parameters that result in a WT Switching-fraction estimate of 12% specify the solid phenotypic boundaries (base). Decreasing the fold-amplification of Tat feedback (reduced feedback, short dashed lines) shifts phenotypic boundaries to the right, while impaired reinitiation (long dashed lines) has little effect on phenotypic boundaries. (B) Estimated Switching fractions for the sets of Tat feedback parameters used in (A), normalized by the predicted WT Switching fraction for the base set of parameters (solid line). (C) Sample Switching (grey) and Bright (black) distributions for the base set of Tat feedback parameters (solid) and for impaired reinitiation parameters (dashed). The degree of transcriptional reinitiation impairment was chosen to produce a comparable shift in Bright phenotype as the parameters for reduced feedback (A–B). The model extension to include transcriptional reinitiation was implemented by a simple rescaling of model parameters according to: (rescaled basal transcription rate); (rescaled amplification factor for transactivated transcription rate); (rescaled feedback saturation parameter). Details may be found in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003135#pcbi.1003135.s008" target="_blank">Text S1</a>.</p

An <i>in vitro</i> model of HIV gene expression exhibits a distribution of integration-site-dependent phenotypes, including noise-driven Switching phenotypes.

<p>(A) Schematic of the full-length HIV lentiviral model of the Tat-mediated positive feedback loop (sLTR-Tat-GFP). Viral proteins other than Tat were inactivated and Nef was replaced with GFP. (B–C) Flow cytometry histogram of Jurkat cells infected with a single HIV WT virus for (B) a bulk population with mixed integration positions and (C) sample Jurkat clonal populations, each containing a single (different) genomic integration of the WT HIV provirus. Representative Dim and Bright clonal histograms were chosen to span the range of fluorescence means. For Switching phenotypes, representative clonal histograms were chosen from the distribution clusters that were used to define a quantitative Switching criterion. GFP axis range is the same for all histograms. (D) Quantification of the WT Switching fraction based on a stratified sample of clones from the full range of GFP expression (“Full”), and based on a sub-sample of clones sorted from only the Mid region of the bulk fluorescence range (“Mid”). Error bars mark 95% confidence intervals, estimated by a bootstrap method.</p

Selected mutations result in small but significant differences in basal gene expression.

<p>(A) Flow cytometry bulk-infection histograms for Jurkat cell populations. Each cell contains a single (different) integration of the Tat-null vector (sLTR-GFP-TatKO) with a WT LTR promoter (black), or an LTR with an Sp1 site III mutation (red). Uninfected Jurkat histogram is displayed for reference (gray). (B–D) Distribution noise (defined as CV²) versus mean GFP for Sp1 mutant clones sorted and expanded from the bulk populations in (A). (C–D) Clonal histograms were fit with the stochastic gene-expression model in the absence of feedback (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003135#pcbi-1003135-g002" target="_blank">Figure 2A</a>), and best-fit parameters were calculated for (C) transcriptional burst size and (D) transcriptional burst frequency. Each point in B–D represents a single-integration clone from a WT (gray) or Sp1 mutant (red) infection.</p