Mechanisms of CD8+ T Cell Mediated Virus Inhibition in HIV-1 Virus Controllers

CD8+ T cells are associated with long term control of virus replication to low or undetectable levels in a population of HIV+ therapy-naïve individuals known as virus controllers (VCs; <5000 RNA copies/ml and CD4+ lymphocyte counts >400 cells/µl). These subjects' ability to control viremia in the absence of therapy makes them the gold standard for the type of CD8+ T-cell response that should be induced with a vaccine. Studying the regulation of CD8+ T cells responses in these VCs provides the opportunity to discover mechanisms of durable control of HIV-1. Previous research has shown that the CD8+ T cell population in VCs is heterogeneous in its ability to inhibit virus replication and distinct T cells are responsible for virus inhibition. Further defining both the functional properties and regulation of the specific features of the select CD8+ T cells responsible for potent control of viremia the in VCs would enable better evaluation of T cell-directed vaccine strategies and may inform the design of new therapies.

Here we discuss the progress made in elucidating the features and regulation of CD8+ T cell response in virus controllers. We first detail the development of assays to quantify CD8+ T cells' ability to inhibit virus replication. This includes the use of a multi-clade HIV-1 panel which can subsequently be used as a tool for evaluation of T cell directed vaccines. We used these assays to evaluate the CD8+ response among cohorts of HIV-1 seronegative, HIV-1 acutely infected, and HIV-1 chronically infected (both VC and chronic viremic) patients. Contact and soluble CD8+ T cell virus inhibition assays (VIAs) are able to distinguish these patient groups based on the presence and magnitude of the responses. When employed in conjunction with peptide stimulation, the soluble assay reveals peptide stimulation induces CD8+ T cell responses with a prevalence of Gag p24 and Nef specificity among the virus controllers tested. Given this prevalence, we aimed to determine the gene expression profile of Gag p24-, Nef-, and unstimulated CD8+ T cells. RNA was isolated from CD8+ T-cells from two virus controllers with strong virus inhibition and one seronegative donor after a 5.5 hour stimulation period then analyzed using the Illumina Human BeadChip platform (Duke Center for Human Genome Variation). Analysis revealed that 565 (242 Nef and 323 Gag) genes were differentially expressed in CD8+ T-cells that were able to inhibit virus replication compared to those that could not. We compared the differentially expressed genes to published data sets from other CD8+ T-cell effector function experiments focusing our analysis on the most recurring genes with immunological, gene regulatory, apoptotic or unknown functions. The most commonly identified gene in these studies was TNFRSF9. Using PCR in a larger cohort of virus controllers we confirmed the up-regulation of TNFRSF9 in Gag p24 and Nef-specific CD8+ T cell mediated virus inhibition. We also observed increase in the mRNA encoding antiviral cytokines macrophage inflammatory proteins (MIP-1α, MIP-1αP, MIP-1β), interferon gamma (IFN-γ), granulocyte-macrophage colony-stimulating factor (GM-CSF), and recently identified lymphotactin (XCL1).

Our previous work suggests the CD8+ T-cell response to HIV-1 can be regulated at the level of gene regulation. Because RNA abundance is modulated by transcription of new mRNAs and decay of new and existing RNA we aimed to evaluate the net rate of transcription and mRNA decay for the cytokines we identified as differentially regulated. To estimate rate of mRNA synthesis and decay, we stimulated isolated CD8+ T-cells with Gag p24 and Nef peptides adding 4-thiouridine (4SU) during the final hour of stimulation, allowing for separation of RNA made during the final hour of stimulation. Subsequent PCR of RNA isolated from these cells, allowed us to determine how much mRNA was made for our genes of interest during the final hour which we used to calculate rate of transcription. To assess if stimulation caused a change in RNA stability, we calculated the decay rates of these mRNA over time. In Gag p24 and Nef stimulated T cells , the abundance of the mRNA of many of the cytokines examined was dependent on changes in both transcription and mRNA decay with evidence for potential differences in the regulation of mRNA between Nef and Gag specific CD8+ T cells. The results were highly reproducible in that in one subject that was measured in three independent experiments the results were concordant.

This data suggests that mRNA stability, in addition to transcription, is key in regulating the direct anti-HIV-1 function of antigen-specific memory CD8+ T cells by enabling rapid recall of anti-HIV-1 effector functions, namely the production and increased stability of antiviral cytokines. We have started to uncover the mechanisms employed by CD8+ T cell subsets with antigen-specific anti-HIV-1 activity, in turn, enhancing our ability to inhibit virus replication by informing both cure strategies and HIV-1 vaccine designs that aim to reduce transmission and can aid in blocking HIV-1 acquisition.

Advances in understanding mechanisms of thalamic relays in cognition and behavior.

The main impetus for a mini-symposium on corticothalamic interrelationships was the recent number of studies highlighting the role of the thalamus in aspects of cognition beyond sensory processing. The thalamus contributes to a range of basic cognitive behaviors that include learning and memory, inhibitory control, decision-making, and the control of visual orienting responses. Its functions are deeply intertwined with those of the better studied cortex, although the principles governing its coordination with the cortex remain opaque, particularly in higher-level aspects of cognition. How should the thalamus be viewed in the context of the rest of the brain? Although its role extends well beyond relaying of sensory information from the periphery, the main function of many of its subdivisions does appear to be that of a relay station, transmitting neural signals primarily to the cerebral cortex from a number of brain areas. In cognition, its main contribution may thus be to coordinate signals between diverse regions of the telencephalon, including the neocortex, hippocampus, amygdala, and striatum. This central coordination is further subject to considerable extrinsic control, for example, inhibition from the basal ganglia, zona incerta, and pretectal regions, and chemical modulation from ascending neurotransmitter systems. What follows is a brief review on the role of the thalamus in aspects of cognition and behavior, focusing on a summary of the topics covered in a mini-symposium held at the Society for Neuroscience meeting, 2014.

Statistical analysis of crystallization database links protein physico-chemical features with crystallization mechanisms.

X-ray crystallography is the predominant method for obtaining atomic-scale information about biological macromolecules. Despite the success of the technique, obtaining well diffracting crystals still critically limits going from protein to structure. In practice, the crystallization process proceeds through knowledge-informed empiricism. Better physico-chemical understanding remains elusive because of the large number of variables involved, hence little guidance is available to systematically identify solution conditions that promote crystallization. To help determine relationships between macromolecular properties and their crystallization propensity, we have trained statistical models on samples for 182 proteins supplied by the Northeast Structural Genomics consortium. Gaussian processes, which capture trends beyond the reach of linear statistical models, distinguish between two main physico-chemical mechanisms driving crystallization. One is characterized by low levels of side chain entropy and has been extensively reported in the literature. The other identifies specific electrostatic interactions not previously described in the crystallization context. Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from solution conditions leading to successful crystallization, the model offers future avenues for optimizing crystallization screens based on partial structural information. The availability of crystallization data coupled with structural outcomes analyzed through state-of-the-art statistical models may thus guide macromolecular crystallization toward a more rational basis.

Different Mechanisms Confer Gradual Control and Memory at Nutrient- and Stress-Regulated Genes in Yeast.

Cells respond to environmental stimuli by fine-tuned regulation of gene expression. Here we investigated the dose-dependent modulation of gene expression at high temporal resolution in response to nutrient and stress signals in yeast. The GAL1 activity in cell populations is modulated in a well-defined range of galactose concentrations, correlating with a dynamic change of histone remodeling and RNA polymerase II (RNAPII) association. This behavior is the result of a heterogeneous induction delay caused by decreasing inducer concentrations across the population. Chromatin remodeling appears to be the basis for the dynamic GAL1 expression, because mutants with impaired histone dynamics show severely truncated dose-response profiles. In contrast, the GRE2 promoter operates like a rapid off/on switch in response to increasing osmotic stress, with almost constant expression rates and exclusively temporal regulation of histone remodeling and RNAPII occupancy. The Gal3 inducer and the Hog1 mitogen-activated protein (MAP) kinase seem to determine the different dose-response strategies at the two promoters. Accordingly, GAL1 becomes highly sensitive and dose independent if previously stimulated because of residual Gal3 levels, whereas GRE2 expression diminishes upon repeated stimulation due to acquired stress resistance. Our analysis reveals important differences in the way dynamic signals create dose-sensitive gene expression outputs.

Dietary inference from upper and lower molar morphology in platyrrhine primates.

The correlation between diet and dental topography is of importance to paleontologists seeking to diagnose ecological adaptations in extinct taxa. Although the subject is well represented in the literature, few studies directly compare methods or evaluate dietary signals conveyed by both upper and lower molars. Here, we address this gap in our knowledge by comparing the efficacy of three measures of functional morphology for classifying an ecologically diverse sample of thirteen medium- to large-bodied platyrrhines by diet category (e.g., folivore, frugivore, hard object feeder). We used Shearing Quotient (SQ), an index derived from linear measurements of molar cutting edges and two indices of crown surface topography, Occlusal Relief (OR) and Relief Index (RFI). Using SQ, OR, and RFI, individuals were then classified by dietary category using Discriminate Function Analysis. Both upper and lower molar variables produce high classification rates in assigning individuals to diet categories, but lower molars are consistently more successful. SQs yield the highest classification rates. RFI and OR generally perform above chance. Upper molar RFI has a success rate below the level of chance. Adding molar length enhances the discriminatory power for all variables. We conclude that upper molar SQs are useful for dietary reconstruction, especially when combined with body size information. Additionally, we find that among our sample of platyrrhines, SQ remains the strongest predictor of diet, while RFI is less useful at signaling dietary differences in absence of body size information. The study demonstrates new ways for inferring the diets of extinct platyrrhine primates when both upper and lower molars are available, or, for taxa known only from upper molars. The techniques are useful in reconstructing diet in stem representatives of anthropoid clade, who share key aspects of molar morphology with extant platyrrhines.

Two Distinct Moral Mechanisms for Ascribing and Denying Intentionality.

Philosophers and legal scholars have long theorized about how intentionality serves as a critical input for morality and culpability, but the emerging field of experimental philosophy has revealed a puzzling asymmetry. People judge actions leading to negative consequences as being more intentional than those leading to positive ones. The implications of this asymmetry remain unclear because there is no consensus regarding the underlying mechanism. Based on converging behavioral and neural evidence, we demonstrate that there is no single underlying mechanism. Instead, two distinct mechanisms together generate the asymmetry. Emotion drives ascriptions of intentionality for negative consequences, while the consideration of statistical norms leads to the denial of intentionality for positive consequences. We employ this novel two-mechanism model to illustrate that morality can paradoxically shape judgments of intentionality. This is consequential for mens rea in legal practice and arguments in moral philosophy pertaining to terror bombing, abortion, and euthanasia among others.

Influence of network topology and data collection on network inference.

We recently developed an approach for testing the accuracy of network inference algorithms by applying them to biologically realistic simulations with known network topology. Here, we seek to determine the degree to which the network topology and data sampling regime influence the ability of our Bayesian network inference algorithm, NETWORKINFERENCE, to recover gene regulatory networks. NETWORKINFERENCE performed well at recovering feedback loops and multiple targets of a regulator with small amounts of data, but required more data to recover multiple regulators of a gene. When collecting the same number of data samples at different intervals from the system, the best recovery was produced by sampling intervals long enough such that sampling covered propagation of regulation through the network but not so long such that intervals missed internal dynamics. These results further elucidate the possibilities and limitations of network inference based on biological data.

Evaluating functional network inference using simulations of complex biological systems.

MOTIVATION: Although many network inference algorithms have been presented in the bioinformatics literature, no suitable approach has been formulated for evaluating their effectiveness at recovering models of complex biological systems from limited data. To overcome this limitation, we propose an approach to evaluate network inference algorithms according to their ability to recover a complex functional network from biologically reasonable simulated data. RESULTS: We designed a simulator to generate data representing a complex biological system at multiple levels of organization: behaviour, neural anatomy, brain electrophysiology, and gene expression of songbirds. About 90% of the simulated variables are unregulated by other variables in the system and are included simply as distracters. We sampled the simulated data at intervals as one would sample from a biological system in practice, and then used the sampled data to evaluate the effectiveness of an algorithm we developed for functional network inference. We found that our algorithm is highly effective at recovering the functional network structure of the simulated system-including the irrelevance of unregulated variables-from sampled data alone. To assess the reproducibility of these results, we tested our inference algorithm on 50 separately simulated sets of data and it consistently recovered almost perfectly the complex functional network structure underlying the simulated data. To our knowledge, this is the first approach for evaluating the effectiveness of functional network inference algorithms at recovering models from limited data. Our simulation approach also enables researchers a priori to design experiments and data-collection protocols that are amenable to functional network inference.

Computational Inference of Genome-Wide Protein-DNA Interactions Using High-Throughput Genomic Data

Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape.

We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations.

We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites.

Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets.

This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.

Evidence of various mechanisms of Cd sequestration in the hyperaccumulator Arabidopsis halleri, the non accumulator Arabidopsis lyrata and their progenies by combined synchrotron-based techniques

Arabidopsis halleri is a model plant for Zn and Cd hyperaccumulation. The objective of this study was to determine the relationship between the chemical forms of Cd, its distribution in leaves, and Cd accumulation and tolerance. An interspecific cross was carried out between A. halleri and the non-tolerant and non-hyperaccumulating relative A. lyrata providing progenies segregating for Cd tolerance and accumulation. Cd speciation and distribution were investigated using X-ray absorption spectroscopy and microfocused X-ray fluorescence. In A. lyrata and non-tolerant progenies, Cd was coordinated by S atoms only or with a small contribution of O groups. Interestingly, the proportion of O ligands increased in A. halleri and tolerant progenies, and they were predominant in most of them, while S ligands were still present. Therefore, the binding of Cd with O ligands was associated with Cd tolerance. In A. halleri, Cd was mainly located in the xylem, phloem, and mesophyll tissue, suggesting a reallocation process for Cd within the plant. The distribution of the metal at the cell level was further discussed. In A. lyrata, the vascular bundles were also Cd enriched, but the epidermis was richer in Cd as compared with the mesophyll. Cd was identified in trichomes of both species. This work demonstrated that both Cd speciation and localization were related to the tolerance character of the plant.

[Book review] Chance encounters: a first course in data analysis and inference

Book review of: Chance Encounters: A First Course in Data Analysis and Inference by Christopher J. Wild and George A.F. Seber 2000, John Wiley & Sons Inc. Hard-bound, xviii + 612 pp ISBN 0-471-32936-3

Use of a coupled mechanical-acoustic computational model to identify failure mechanisms in paper production

In this paper, a couple mechanical-acoustic system of equations is solved to determine the relationship between emitted sound and damage mechanisims in paper under controlled stress conditions. The simple classical expression describing the frequency of a plucked string to its material properties is used to generate a numberical representation of the microscopic structue of the paper, and the resulting numerical model is then used to simulate the vibration of a range of simple fibre structures when undergoing two distinct types of damange mechanisms: (a)fibre/fibre bond failure, (b) fibre failure. The numercial results are analysed to determine whether there is any detectable systematic difference between the resulting acoustic emissions of the two damage processes. Fourier techniques are then used to compare th computeed results against experimental measurements. Distinct frequency components identifying each type of damage are shown to exist, and in this respect theory and experiments show good correspondece. Hence, it is shown, that althrough the mathematical model represents a grossly-simplified view of the complex structure of the paper, it nevertheless provides a good understanding of the underlying micro-mechanisms characterising its proeperties as a stress-resisting structure. Use of the model and acoompanying software will enable operators to identify approaching failure conditions in the continuous production of paper from emitted sound signals and take preventative action.

Identifying the damage mechanisms in paper using the acoustic emission monitoring technique

This paper investigates the use of the acoustic emission (AE) monitoring technique for use in identifying the damage mechanisms present in paper associated with its production process. The microscopic structure of paper consists of a random mesh of paper fibres connected by hydrogen bonds. This implies the existence of two damage mechanisms, the failure of a fibre-fibre bond and the failure of a fibre. This paper describes a hybrid mathematical model which couples the mechanics of the mass-spring model to the acoustic wave propagation model for use in generating the acoustic signal emitted by complex structures of paper fibres under strain. The derivation of the mass-spring model can be found in [1,2], with details of the acoustic wave equation found in [3,4]. The numerical implementation of the vibro-acoustic model is discussed in detail with particular emphasis on the damping present in the numerical model. The hybrid model uses an implicit solver which intrinsically introduces artificial damping to the solution. The artificial damping is shown to affect the frequency response of the mass-spring model, therefore certain restrictions on the simulation time step must be enforced so that the model produces physically accurate results. The hybrid mathematical model is used to simulate small fibre networks to provide information on the acoustic response of each damage mechanism. The simulated AEs are then analysed using a continuous wavelet transform (CWT), described in [5], which provides a two dimensional time-frequency representation of the signal. The AEs from the two damage mechanisms show different characteristics in the CWT so that it is possible to define a fibre-fibre bond failure by the criteria listed below. The dominant frequency components of the AE must be at approximately 250 kHz or 750 kHz. The strongest frequency component may be at either approximately 250 kHz or 750 kHz. The duration of the frequency component at approximately 250 kHz is longer than that of the frequency component at approximately 750 kHz. Similarly, the criteria for identifying a fibre failure are given below. The dominant frequency component of the AE must be greater than 800 kHz. The duration of the dominant frequency component must be less than 5.00E-06 seconds. The dominant frequency component must be present at the front of the AE. Essentially, the failure of a fibre-fibre bond produces a low frequency wave and the failure of a fibre produces a high frequency pulse. Using this theoretical criteria, it is now possible to train an intelligent classifier such as the Self-Organising Map (SOM) [6] using the experimental data. First certain features must be extracted from the CWTs of the AEs for use in training the SOM. For this work, each CWT is divided into 200 windows of 5E-06s in duration covering a 100 kHz frequency range. The power ratio for each windows is then calculated and used as a feature. Having extracted the features from the AEs, the SOM can now be trained, but care is required so that the both damage mechanisms are adequately represented in the training set. This is an issue with paper as the failure of the fibre-fibre bonds is the prevalent damage mechanism. Once a suitable training set is found, the SOM can be trained and its performance analysed. For the SOM described in this work, there is a good chance that it will correctly classify the experimental AEs.

An analysis of the changes to the mechanical and acoustical properties of paper caused by the damage mechanisms present in the fibre structure

This paper will analyse two of the likely damage mechanisms present in a paper fibre matrix when placed under controlled stress conditions: fibre/fibre bond failure and fibre failure. The failure process associated with each damage mechanism will be presented in detail focusing on the change in mechanical and acoustic properties of the surrounding fibre structure before and after failure. To present this complex process mathematically, geometrically simple fibre arrangements will be chosen based on certain assumptions regarding the structure and strength of paper, to model the damage mechanisms. The fibre structures are then formulated in terms of a hybrid vibro-acoustic model based on a coupled mass/spring system and the pressure wave equation. The model will be presented in detail in the paper. The simulation of the simple fibre structures serves two purposes; it highlights the physical and acoustic differences of each damage mechanism before and after failure, and also shows the differences in the two damage mechanisms when compared with one another. The results of the simulations are given in the form of pressure wave contours, time-frequency graphs and the Continuous Wavelet Transform (CWT) diagrams. The analysis of the results leads to criteria by which the two damage mechanisms can be identified. Using these criteria it was possible to verify the results of the simulations against experimental acoustic data. The models developed in this study are of specific practical interest in the paper-making industry, where acoustic sensors may be used to monitor continuous paper production. The same techniques may be adopted more generally to correlate acoustic signals to damage mechanisms in other fibre-based structures.