Molecular characterization of the contribution of autophagy to antigen presentation using quantitative proteomics

L’autophagie est une voie hautement conservée de dégradation lysosomale des constituants cellulaires qui est essentiel à l’homéostasie cellulaire et contribue à l’apprêtement et à la présentation des antigènes. Les rôles relativement récents de l'autophagie dans l'immunité innée et acquise sous-tendent de nouveaux paradigmes immunologiques pouvant faciliter le développement de nouvelles thérapies où la dérégulation de l’autophagie est associée à des maladies auto-immunes. Cependant, l'étude in vivo de la réponse autophagique est difficile en raison du nombre limité de méthodes d'analyse pouvant fournir une définition dynamique des protéines clés impliquées dans cette voie. En conséquence, nous avons développé un programme de recherche en protéomique intégrée afin d’identifier et de quantifier les proteines associées à l'autophagie et de déterminer les mécanismes moléculaires régissant les fonctions de l’autophagosome dans la présentation antigénique en utilisant une approche de biologie des systèmes. Pour étudier comment l'autophagie et la présentation antigénique sont activement régulés dans les macrophages, nous avons d'abord procédé à une étude protéomique à grande échelle sous différentes conditions connues pour stimuler l'autophagie, tels l’activation par les cytokines et l’infection virale. La cytokine tumor necrosis factor-alpha (TNF-alpha) est l'une des principales cytokines pro-inflammatoires qui intervient dans les réactions locales et systémiques afin de développer une réponse immune adaptative. La protéomique quantitative d'extraits membranaires de macrophages contrôles et stimulés avec le TNF-alpha a révélé que l'activation des macrophages a entrainé la dégradation de protéines mitochondriales et des changements d’abondance de plusieurs protéines impliquées dans le trafic vésiculaire et la réponse immunitaire. Nous avons constaté que la dégradation des protéines mitochondriales était sous le contrôle de la voie ATG5, et était spécifique au TNF-alpha. En outre, l’utilisation d’un nouveau système de présentation antigènique, nous a permi de constater que l'induction de la mitophagie par le TNF-alpha a entrainée l’apprêtement et la présentation d’antigènes mitochondriaux par des molécules du CMH de classe I, contribuant ainsi la variation du répertoire immunopeptidomique à la surface cellulaire. Ces résultats mettent en évidence un rôle insoupçonné du TNF-alpha dans la mitophagie et permet une meilleure compréhension des mécanismes responsables de la présentation d’auto-antigènes par les molécules du CMH de classe I. Une interaction complexe existe également entre infection virale et l'autophagie. Récemment, notre laboratoire a fourni une première preuve suggérant que la macroautophagie peut contribuer à la présentation de protéines virales par les molécules du CMH de classe I lors de l’infection virale par l'herpès simplex virus de type 1 (HSV-1). Le virus HSV1 fait parti des virus humains les plus complexes et les plus répandues. Bien que la composition des particules virales a été étudiée précédemment, on connaît moins bien l'expression de l'ensemble du protéome viral lors de l’infection des cellules hôtes. Afin de caractériser les changements dynamiques de l’expression des protéines virales lors de l’infection, nous avons analysé par LC-MS/MS le protéome du HSV1 dans les macrophages infectés. Ces analyses nous ont permis d’identifier un total de 67 protéines virales structurales et non structurales (82% du protéome HSV1) en utilisant le spectromètre de masse LTQ-Orbitrap. Nous avons également identifié 90 nouveaux sites de phosphorylation et de dix nouveaux sites d’ubiquitylation sur différentes protéines virales. Suite à l’ubiquitylation, les protéines virales peuvent se localiser au noyau ou participer à des événements de fusion avec la membrane nucléaire, suggérant ainsi que cette modification pourrait influer le trafic vésiculaire des protéines virales. Le traitement avec des inhibiteurs de la réplication de l'ADN induit des changements sur l'abondance et la modification des protéines virales, mettant en évidence l'interdépendance des protéines virales au cours du cycle de vie du virus. Compte tenu de l'importance de la dynamique d'expression, de l’ubiquitylation et la phosphorylation sur la fonction des proteines virales, ces résultats ouvriront la voie vers de nouvelles études sur la biologie des virus de l'herpès. Fait intéressant, l'infection HSV1 dans les macrophages déclenche une nouvelle forme d'autophagie qui diffère remarquablement de la macroautophagie. Ce processus, appelé autophagie associée à l’enveloppe nucléaire (nuclear envelope derived autophagy, NEDA), conduit à la formation de vésicules membranaires contenant 4 couches lipidiques provenant de l'enveloppe nucléaire où on retrouve une grande proportion de certaines protéines virales, telle la glycoprotéine B. Les mécanismes régissant NEDA et leur importance lors de l’infection virale sont encore méconnus. En utilisant un essai de présentation antigénique, nous avons pu montrer que la voie NEDA est indépendante d’ATG5 et participe à l’apprêtement et la présentation d’antigènes viraux par le CMH de classe I. Pour comprendre l'implication de NEDA dans la présentation des antigènes, il est essentiel de caractériser le protéome des autophagosomes isolés à partir de macrophages infectés par HSV1. Aussi, nous avons développé une nouvelle approche de fractionnement basé sur l’isolation de lysosomes chargés de billes de latex, nous permettant ainsi d’obtenir des extraits cellulaires enrichis en autophagosomes. Le transfert des antigènes HSV1 dans les autophagosomes a été determine par protéomique quantitative. Les protéines provenant de l’enveloppe nucléaire ont été préférentiellement transférées dans les autophagosome lors de l'infection des macrophages par le HSV1. Les analyses protéomiques d’autophagosomes impliquant NEDA ou la macroautophagie ont permis de decouvrir des mécanismes jouant un rôle clé dans l’immunodominance de la glycoprotéine B lors de l'infection HSV1. Ces analyses ont également révélées que diverses voies autophagiques peuvent être induites pour favoriser la capture sélective de protéines virales, façonnant de façon dynamique la nature de la réponse immunitaire lors d'une infection. En conclusion, l'application des méthodes de protéomique quantitative a joué un rôle clé dans l'identification et la quantification des protéines ayant des rôles importants dans la régulation de l'autophagie chez les macrophages, et nous a permis d'identifier les changements qui se produisent lors de la formation des autophagosomes lors de maladies inflammatoires ou d’infection virale. En outre, notre approche de biologie des systèmes, qui combine la protéomique quantitative basée sur la spectrométrie de masse avec des essais fonctionnels tels la présentation antigénique, nous a permis d’acquérir de nouvelles connaissances sur les mécanismes moléculaires régissant les fonctions de l'autophagie lors de la présentation antigénique. Une meilleure compréhension de ces mécanismes permettra de réduire les effets nuisibles de l'immunodominance suite à l'infection virale ou lors du développement du cancer en mettant en place une réponse immunitaire appropriée.

Estimators and Tests based on Likelihood-Depth with Application to Weibull Distribution, Gaussian and Gumbel Copula

In dieser Arbeit werden mithilfe der Likelihood-Tiefen, eingeführt von Mizera und Müller (2004), (ausreißer-)robuste Schätzfunktionen und Tests für den unbekannten Parameter einer stetigen Dichtefunktion entwickelt. Die entwickelten Verfahren werden dann auf drei verschiedene Verteilungen angewandt. Für eindimensionale Parameter wird die Likelihood-Tiefe eines Parameters im Datensatz als das Minimum aus dem Anteil der Daten, für die die Ableitung der Loglikelihood-Funktion nach dem Parameter nicht negativ ist, und dem Anteil der Daten, für die diese Ableitung nicht positiv ist, berechnet. Damit hat der Parameter die größte Tiefe, für den beide Anzahlen gleich groß sind. Dieser wird zunächst als Schätzer gewählt, da die Likelihood-Tiefe ein Maß dafür sein soll, wie gut ein Parameter zum Datensatz passt. Asymptotisch hat der Parameter die größte Tiefe, für den die Wahrscheinlichkeit, dass für eine Beobachtung die Ableitung der Loglikelihood-Funktion nach dem Parameter nicht negativ ist, gleich einhalb ist. Wenn dies für den zu Grunde liegenden Parameter nicht der Fall ist, ist der Schätzer basierend auf der Likelihood-Tiefe verfälscht. In dieser Arbeit wird gezeigt, wie diese Verfälschung korrigiert werden kann sodass die korrigierten Schätzer konsistente Schätzungen bilden. Zur Entwicklung von Tests für den Parameter, wird die von Müller (2005) entwickelte Simplex Likelihood-Tiefe, die eine U-Statistik ist, benutzt. Es zeigt sich, dass für dieselben Verteilungen, für die die Likelihood-Tiefe verfälschte Schätzer liefert, die Simplex Likelihood-Tiefe eine unverfälschte U-Statistik ist. Damit ist insbesondere die asymptotische Verteilung bekannt und es lassen sich Tests für verschiedene Hypothesen formulieren. Die Verschiebung in der Tiefe führt aber für einige Hypothesen zu einer schlechten Güte des zugehörigen Tests. Es werden daher korrigierte Tests eingeführt und Voraussetzungen angegeben, unter denen diese dann konsistent sind. Die Arbeit besteht aus zwei Teilen. Im ersten Teil der Arbeit wird die allgemeine Theorie über die Schätzfunktionen und Tests dargestellt und zudem deren jeweiligen Konsistenz gezeigt. Im zweiten Teil wird die Theorie auf drei verschiedene Verteilungen angewandt: Die Weibull-Verteilung, die Gauß- und die Gumbel-Copula. Damit wird gezeigt, wie die Verfahren des ersten Teils genutzt werden können, um (robuste) konsistente Schätzfunktionen und Tests für den unbekannten Parameter der Verteilung herzuleiten. Insgesamt zeigt sich, dass für die drei Verteilungen mithilfe der Likelihood-Tiefen robuste Schätzfunktionen und Tests gefunden werden können. In unverfälschten Daten sind vorhandene Standardmethoden zum Teil überlegen, jedoch zeigt sich der Vorteil der neuen Methoden in kontaminierten Daten und Daten mit Ausreißern.

Hybrid optimization schemes for quantum control

Optimal control theory is a powerful tool for solving control problems in quantum mechanics, ranging from the control of chemical reactions to the implementation of gates in a quantum computer. Gradient-based optimization methods are able to find high fidelity controls, but require considerable numerical effort and often yield highly complex solutions. We propose here to employ a two-stage optimization scheme to significantly speed up convergence and achieve simpler controls. The control is initially parametrized using only a few free parameters, such that optimization in this pruned search space can be performed with a simplex method. The result, considered now simply as an arbitrary function on a time grid, is the starting point for further optimization with a gradient-based method that can quickly converge to high fidelities. We illustrate the success of this hybrid technique by optimizing a geometric phase gate for two superconducting transmon qubits coupled with a shared transmission line resonator, showing that a combination of Nelder-Mead simplex and Krotov’s method yields considerably better results than either one of the two methods alone.

Hilbert space on probability density functions with Aitchison geometry

Compositional data analysis motivated the introduction of a complete Euclidean structure in the simplex of D parts. This was based on the early work of J. Aitchison (1986) and completed recently when Aitchinson distance in the simplex was associated with an inner product and orthonormal bases were identified (Aitchison and others, 2002; Egozcue and others, 2003). A partition of the support of a random variable generates a composition by assigning the probability of each interval to a part of the composition. One can imagine that the partition can be refined and the probability density would represent a kind of continuous composition of probabilities in a simplex of infinitely many parts. This intuitive idea would lead to a Hilbert-space of probability densities by generalizing the Aitchison geometry for compositions in the simplex into the set probability densities

When a data set can be considered compositional?

Traditionally, compositional data has been identified with closed data, and the simplex has been considered as the natural sample space of this kind of data. In our opinion, the emphasis on the constrained nature of compositional data has contributed to mask its real nature. More crucial than the constraining property of compositional data is the scale-invariant property of this kind of data. Indeed, when we are considering only few parts of a full composition we are not working with constrained data but our data are still compositional. We believe that it is necessary to give a more precise definition of composition. This is the aim of this oral contribution

Aitchison Geometry for Probability and Likelihood as a new approach to mathematical statistics

The Aitchison vector space structure for the simplex is generalized to a Hilbert space structure A2(P) for distributions and likelihoods on arbitrary spaces. Central notations of statistics, such as Information or Likelihood, can be identified in the algebraical structure of A2(P) and their corresponding notions in compositional data analysis, such as Aitchison distance or centered log ratio transform. In this way very elaborated aspects of mathematical statistics can be understood easily in the light of a simple vector space structure and of compositional data analysis. E.g. combination of statistical information such as Bayesian updating, combination of likelihood and robust M-estimation functions are simple additions/ perturbations in A2(Pprior). Weighting observations corresponds to a weighted addition of the corresponding evidence. Likelihood based statistics for general exponential families turns out to have a particularly easy interpretation in terms of A2(P). Regular exponential families form finite dimensional linear subspaces of A2(P) and they correspond to finite dimensional subspaces formed by their posterior in the dual information space A2(Pprior). The Aitchison norm can identified with mean Fisher information. The closing constant itself is identified with a generalization of the cummulant function and shown to be Kullback Leiblers directed information. Fisher information is the local geometry of the manifold induced by the A2(P) derivative of the Kullback Leibler information and the space A2(P) can therefore be seen as the tangential geometry of statistical inference at the distribution P. The discussion of A2(P) valued random variables, such as estimation functions or likelihoods, give a further interpretation of Fisher information as the expected squared norm of evidence and a scale free understanding of unbiased reasoning

CoDa-dendrogram: A new exploratory too

The use of orthonormal coordinates in the simplex and, particularly, balance coordinates, has suggested the use of a dendrogram for the exploratory analysis of compositional data. The dendrogram is based on a sequential binary partition of a compositional vector into groups of parts. At each step of a partition, one group of parts is divided into two new groups, and a balancing axis in the simplex between both groups is defined. The set of balancing axes constitutes an orthonormal basis, and the projections of the sample on them are orthogonal coordinates. They can be represented in a dendrogram-like graph showing: (a) the way of grouping parts of the compositional vector; (b) the explanatory role of each subcomposition generated in the partition process; (c) the decomposition of the total variance into balance components associated with each binary partition; (d) a box-plot of each balance. This representation is useful to help the interpretation of balance coordinates; to identify which are the most explanatory coordinates; and to describe the whole sample in a single diagram independently of the number of parts of the sample

Markov chain montecarlo method applied to rounding zeros of compositional data: first approach

As stated in Aitchison (1986), a proper study of relative variation in a compositional data set should be based on logratios, and dealing with logratios excludes dealing with zeros. Nevertheless, it is clear that zero observations might be present in real data sets, either because the corresponding part is completely absent –essential zeros– or because it is below detection limit –rounded zeros. Because the second kind of zeros is usually understood as “a trace too small to measure”, it seems reasonable to replace them by a suitable small value, and this has been the traditional approach. As stated, e.g. by Tauber (1999) and by Martín-Fernández, Barceló-Vidal, and Pawlowsky-Glahn (2000), the principal problem in compositional data analysis is related to rounded zeros. One should be careful to use a replacement strategy that does not seriously distort the general structure of the data. In particular, the covariance structure of the involved parts –and thus the metric properties– should be preserved, as otherwise further analysis on subpopulations could be misleading. Following this point of view, a non-parametric imputation method is introduced in Martín-Fernández, Barceló-Vidal, and Pawlowsky-Glahn (2000). This method is analyzed in depth by Martín-Fernández, Barceló-Vidal, and Pawlowsky-Glahn (2003) where it is shown that the theoretical drawbacks of the additive zero replacement method proposed in Aitchison (1986) can be overcome using a new multiplicative approach on the non-zero parts of a composition. The new approach has reasonable properties from a compositional point of view. In particular, it is “natural” in the sense that it recovers the “true” composition if replacement values are identical to the missing values, and it is coherent with the basic operations on the simplex. This coherence implies that the covariance structure of subcompositions with no zeros is preserved. As a generalization of the multiplicative replacement, in the same paper a substitution method for missing values on compositional data sets is introduced

Compositional data analysis with R

Examples of compositional data. The simplex, a suitable sample space for compositional data and Aitchison's geometry. R, a free language and environment for statistical computing and graphics

Exploration of geological variability and possible processes through the use of compositional data analysis: an example using scottish metamorphosed

Developments in the statistical analysis of compositional data over the last two decades have made possible a much deeper exploration of the nature of variability, and the possible processes associated with compositional data sets from many disciplines. In this paper we concentrate on geochemical data sets. First we explain how hypotheses of compositional variability may be formulated within the natural sample space, the unit simplex, including useful hypotheses of subcompositional discrimination and specific perturbational change. Then we develop through standard methodology, such as generalised likelihood ratio tests, statistical tools to allow the systematic investigation of a complete lattice of such hypotheses. Some of these tests are simple adaptations of existing multivariate tests but others require special construction. We comment on the use of graphical methods in compositional data analysis and on the ordination of specimens. The recent development of the concept of compositional processes is then explained together with the necessary tools for a staying- in-the-simplex approach, namely compositional singular value decompositions. All these statistical techniques are illustrated for a substantial compositional data set, consisting of 209 major-oxide and rare-element compositions of metamorphosed limestones from the Northeast and Central Highlands of Scotland. Finally we point out a number of unresolved problems in the statistical analysis of compositional processes

Monitoring procedures in environmental geochemistry and compositional data analysis theory

First discussion on compositional data analysis is attributable to Karl Pearson, in 1897. However, notwithstanding the recent developments on algebraic structure of the simplex, more than twenty years after Aitchison’s idea of log-transformations of closed data, scientific literature is again full of statistical treatments of this type of data by using traditional methodologies. This is particularly true in environmental geochemistry where besides the problem of the closure, the spatial structure (dependence) of the data have to be considered. In this work we propose the use of log-contrast values, obtained by a simplicial principal component analysis, as LQGLFDWRUV of given environmental conditions. The investigation of the log-constrast frequency distributions allows pointing out the statistical laws able to generate the values and to govern their variability. The changes, if compared, for example, with the mean values of the random variables assumed as models, or other reference parameters, allow defining monitors to be used to assess the extent of possible environmental contamination. Case study on running and ground waters from Chiavenna Valley (Northern Italy) by using Na+, K+, Ca2+, Mg2+, HCO3-, SO4 2- and Cl- concentrations will be illustrated

New insights on river water chemistry by using non-centred simplicial principal component analysis: a case study

The use of perturbation and power transformation operations permits the investigation of linear processes in the simplex as in a vectorial space. When the investigated geochemical processes can be constrained by the use of well-known starting point, the eigenvectors of the covariance matrix of a non-centred principal component analysis allow to model compositional changes compared with a reference point. The results obtained for the chemistry of water collected in River Arno (central-northern Italy) have open new perspectives for considering relative changes of the analysed variables and to hypothesise the relative effect of different acting physical-chemical processes, thus posing the basis for a quantitative modelling

The chemical variability at the surface of Mars: implications for exogenic processes

The chemical composition of sediments and rocks, as well as their distribution at the Martian surface, represent a long term archive of processes, which have formed the planetary surface. A survey of chemical compositions by means of Compositional Data Analysis represents a valuable tool to extract direct evidence for weathering processes and allows to quantify weathering and sedimentation rates. clr-biplot techniques are applied for visualization of chemical relationships across the surface (“chemical maps”). The variability among individual suites of data is further analyzed by means of clr-PCA, in order to extract chemical alteration vectors between fresh rocks and their crusts and for an assessment of different source reservoirs accessible to soil formation. Both techniques are applied to elucidate the influence of remote weathering by combined analysis of several soil forming branches. Vector analysis in the Simplex provides the opportunity to study atmosphere surface interactions, including the role and composition of volcanic gases

Inference of distributional parameters from compositional samples containing nondetects

Low concentrations of elements in geochemical analyses have the peculiarity of being compositional data and, for a given level of significance, are likely to be beyond the capabilities of laboratories to distinguish between minute concentrations and complete absence, thus preventing laboratories from reporting extremely low concentrations of the analyte. Instead, what is reported is the detection limit, which is the minimum concentration that conclusively differentiates between presence and absence of the element. A spatially distributed exhaustive sample is employed in this study to generate unbiased sub-samples, which are further censored to observe the effect that different detection limits and sample sizes have on the inference of population distributions starting from geochemical analyses having specimens below detection limit (nondetects). The isometric logratio transformation is used to convert the compositional data in the simplex to samples in real space, thus allowing the practitioner to properly borrow from the large source of statistical techniques valid only in real space. The bootstrap method is used to numerically investigate the reliability of inferring several distributional parameters employing different forms of imputation for the censored data. The case study illustrates that, in general, best results are obtained when imputations are made using the distribution best fitting the readings above detection limit and exposes the problems of other more widely used practices. When the sample is spatially correlated, it is necessary to combine the bootstrap with stochastic simulation

Compositional analysis of bivariate discrete probabilities

A joint distribution of two discrete random variables with finite support can be displayed as a two way table of probabilities adding to one. Assume that this table has n rows and m columns and all probabilities are non-null. This kind of table can be seen as an element in the simplex of n · m parts. In this context, the marginals are identified as compositional amalgams, conditionals (rows or columns) as subcompositions. Also, simplicial perturbation appears as Bayes theorem. However, the Euclidean elements of the Aitchison geometry of the simplex can also be translated into the table of probabilities: subspaces, orthogonal projections, distances. Two important questions are addressed: a) given a table of probabilities, which is the nearest independent table to the initial one? b) which is the largest orthogonal projection of a row onto a column? or, equivalently, which is the information in a row explained by a column, thus explaining the interaction? To answer these questions three orthogonal decompositions are presented: (1) by columns and a row-wise geometric marginal, (2) by rows and a columnwise geometric marginal, (3) by independent two-way tables and fully dependent tables representing row-column interaction. An important result is that the nearest independent table is the product of the two (row and column)-wise geometric marginal tables. A corollary is that, in an independent table, the geometric marginals conform with the traditional (arithmetic) marginals. These decompositions can be compared with standard log-linear models. Key words: balance, compositional data, simplex, Aitchison geometry, composition, orthonormal basis, arithmetic and geometric marginals, amalgam, dependence measure, contingency table