Collective Organization of Complex Networks: Emergent Scales, Vulnerability, and Targeting of Desired Dynamics

Cuando una colectividad de sistemas dinámicos acoplados mediante una estructura irregular de interacciones evoluciona, se observan dinámicas de gran complejidad y fenómenos emergentes imposibles de predecir a partir de las propiedades de los sistemas individuales. El objetivo principal de esta tesis es precisamente avanzar en nuestra comprensión de la relación existente entre la topología de interacciones y las dinámicas colectivas que una red compleja es capaz de mantener. Siendo este un tema amplio que se puede abordar desde distintos puntos de vista, en esta tesis se han estudiado tres problemas importantes dentro del mismo que están relacionados entre sí. Por un lado, en numerosos sistemas naturales y artificiales que se pueden describir mediante una red compleja la topología no es estática, sino que depende de la dinámica que se desarrolla en la red: un ejemplo son las redes de neuronas del cerebro. En estas redes adaptativas la propia topología emerge como consecuencia de una autoorganización del sistema. Para conocer mejor cómo pueden emerger espontáneamente las propiedades comúnmente observadas en redes reales, hemos estudiado el comportamiento de sistemas que evolucionan según reglas adaptativas locales con base empírica. Nuestros resultados numéricos y analíticos muestran que la autoorganización del sistema da lugar a dos de las propiedades más universales de las redes complejas: a escala mesoscópica, la aparición de una estructura de comunidades, y, a escala macroscópica, la existencia de una ley de potencias en la distribución de las interacciones en la red. El hecho de que estas propiedades aparecen en dos modelos con leyes de evolución cuantitativamente distintas que siguen unos mismos principios adaptativos sugiere que estamos ante un fenómeno que puede ser muy general, y estar en el origen de estas propiedades en sistemas reales. En segundo lugar, proponemos una medida que permite clasificar los elementos de una red compleja en función de su relevancia para el mantenimiento de dinámicas colectivas. En concreto, estudiamos la vulnerabilidad de los distintos elementos de una red frente a perturbaciones o grandes fluctuaciones, entendida como una medida del impacto que estos acontecimientos externos tienen en la interrupción de una dinámica colectiva. Los resultados que se obtienen indican que la vulnerabilidad dinámica es sobre todo dependiente de propiedades locales, por tanto nuestras conclusiones abarcan diferentes topologías, y muestran la existencia de una dependencia no trivial entre la vulnerabilidad y la conectividad de los elementos de una red. Finalmente, proponemos una estrategia de imposición de una dinámica objetivo genérica en una red dada e investigamos su validez en redes con diversas topologías que mantienen regímenes dinámicos turbulentos. Se obtiene como resultado que las redes heterogéneas (y la amplia mayora de las redes reales estudiadas lo son) son las más adecuadas para nuestra estrategia de targeting de dinámicas deseadas, siendo la estrategia muy efectiva incluso en caso de disponer de un conocimiento muy imperfecto de la topología de la red. Aparte de la relevancia teórica para la comprensión de fenómenos colectivos en sistemas complejos, los métodos y resultados propuestos podrán dar lugar a aplicaciones en sistemas experimentales y tecnológicos, como por ejemplo los sistemas neuronales in vitro, el sistema nervioso central (en el estudio de actividades síncronas de carácter patológico), las redes eléctricas o los sistemas de comunicaciones. ABSTRACT The time evolution of an ensemble of dynamical systems coupled through an irregular interaction scheme gives rise to dynamics of great of complexity and emergent phenomena that cannot be predicted from the properties of the individual systems. The main objective of this thesis is precisely to increase our understanding of the interplay between the interaction topology and the collective dynamics that a complex network can support. This is a very broad subject, so in this thesis we will limit ourselves to the study of three relevant problems that have strong connections among them. First, it is a well-known fact that in many natural and manmade systems that can be represented as complex networks the topology is not static; rather, it depends on the dynamics taking place on the network (as it happens, for instance, in the neuronal networks in the brain). In these adaptive networks the topology itself emerges from the self-organization in the system. To better understand how the properties that are commonly observed in real networks spontaneously emerge, we have studied the behavior of systems that evolve according to local adaptive rules that are empirically motivated. Our numerical and analytical results show that self-organization brings about two of the most universally found properties in complex networks: at the mesoscopic scale, the appearance of a community structure, and, at the macroscopic scale, the existence of a power law in the weight distribution of the network interactions. The fact that these properties show up in two models with quantitatively different mechanisms that follow the same general adaptive principles suggests that our results may be generalized to other systems as well, and they may be behind the origin of these properties in some real systems. We also propose a new measure that provides a ranking of the elements in a network in terms of their relevance for the maintenance of collective dynamics. Specifically, we study the vulnerability of the elements under perturbations or large fluctuations, interpreted as a measure of the impact these external events have on the disruption of collective motion. Our results suggest that the dynamic vulnerability measure depends largely on local properties (our conclusions thus being valid for different topologies) and they show a non-trivial dependence of the vulnerability on the connectivity of the network elements. Finally, we propose a strategy for the imposition of generic goal dynamics on a given network, and we explore its performance in networks with different topologies that support turbulent dynamical regimes. It turns out that heterogeneous networks (and most real networks that have been studied belong in this category) are the most suitable for our strategy for the targeting of desired dynamics, the strategy being very effective even when the knowledge on the network topology is far from accurate. Aside from their theoretical relevance for the understanding of collective phenomena in complex systems, the methods and results here discussed might lead to applications in experimental and technological systems, such as in vitro neuronal systems, the central nervous system (where pathological synchronous activity sometimes occurs), communication systems or power grids.

Functional and effective connectivity in MEG. Application to the study of epilepsy

Nuestro cerebro contiene cerca de 1014 sinapsis neuronales. Esta enorme cantidad de conexiones proporciona un entorno ideal donde distintos grupos de neuronas se sincronizan transitoriamente para provocar la aparición de funciones cognitivas, como la percepción, el aprendizaje o el pensamiento. Comprender la organización de esta compleja red cerebral en base a datos neurofisiológicos, representa uno de los desafíos más importantes y emocionantes en el campo de la neurociencia. Se han propuesto recientemente varias medidas para evaluar cómo se comunican las diferentes partes del cerebro a diversas escalas (células individuales, columnas corticales, o áreas cerebrales). Podemos clasificarlos, según su simetría, en dos grupos: por una parte, la medidas simétricas, como la correlación, la coherencia o la sincronización de fase, que evalúan la conectividad funcional (FC); mientras que las medidas asimétricas, como la causalidad de Granger o transferencia de entropía, son capaces de detectar la dirección de la interacción, lo que denominamos conectividad efectiva (EC). En la neurociencia moderna ha aumentado el interés por el estudio de las redes funcionales cerebrales, en gran medida debido a la aparición de estos nuevos algoritmos que permiten analizar la interdependencia entre señales temporales, además de la emergente teoría de redes complejas y la introducción de técnicas novedosas, como la magnetoencefalografía (MEG), para registrar datos neurofisiológicos con gran resolución. Sin embargo, nos hallamos ante un campo novedoso que presenta aun varias cuestiones metodológicas sin resolver, algunas de las cuales trataran de abordarse en esta tesis. En primer lugar, el creciente número de aproximaciones para determinar la existencia de FC/EC entre dos o más señales temporales, junto con la complejidad matemática de las herramientas de análisis, hacen deseable organizarlas todas en un paquete software intuitivo y fácil de usar. Aquí presento HERMES (http://hermes.ctb.upm.es), una toolbox en MatlabR, diseñada precisamente con este fin. Creo que esta herramienta será de gran ayuda para todos aquellos investigadores que trabajen en el campo emergente del análisis de conectividad cerebral y supondrá un gran valor para la comunidad científica. La segunda cuestión practica que se aborda es el estudio de la sensibilidad a las fuentes cerebrales profundas a través de dos tipos de sensores MEG: gradiómetros planares y magnetómetros, esta aproximación además se combina con un enfoque metodológico, utilizando dos índices de sincronización de fase: phase locking value (PLV) y phase lag index (PLI), este ultimo menos sensible a efecto la conducción volumen. Por lo tanto, se compara su comportamiento al estudiar las redes cerebrales, obteniendo que magnetómetros y PLV presentan, respectivamente, redes más densamente conectadas que gradiómetros planares y PLI, por los valores artificiales que crea el problema de la conducción de volumen. Sin embargo, cuando se trata de caracterizar redes epilépticas, el PLV ofrece mejores resultados, debido a la gran dispersión de las redes obtenidas con PLI. El análisis de redes complejas ha proporcionado nuevos conceptos que mejoran caracterización de la interacción de sistemas dinámicos. Se considera que una red está compuesta por nodos, que simbolizan sistemas, cuyas interacciones se representan por enlaces, y su comportamiento y topología puede caracterizarse por un elevado número de medidas. Existe evidencia teórica y empírica de que muchas de ellas están fuertemente correlacionadas entre sí. Por lo tanto, se ha conseguido seleccionar un pequeño grupo que caracteriza eficazmente estas redes, y condensa la información redundante. Para el análisis de redes funcionales, la selección de un umbral adecuado para decidir si un determinado valor de conectividad de la matriz de FC es significativo y debe ser incluido para un análisis posterior, se convierte en un paso crucial. En esta tesis, se han obtenido resultados más precisos al utilizar un test de subrogadas, basado en los datos, para evaluar individualmente cada uno de los enlaces, que al establecer a priori un umbral fijo para la densidad de conexiones. Finalmente, todas estas cuestiones se han aplicado al estudio de la epilepsia, caso práctico en el que se analizan las redes funcionales MEG, en estado de reposo, de dos grupos de pacientes epilépticos (generalizada idiopática y focal frontal) en comparación con sujetos control sanos. La epilepsia es uno de los trastornos neurológicos más comunes, con más de 55 millones de afectados en el mundo. Esta enfermedad se caracteriza por la predisposición a generar ataques epilépticos de actividad neuronal anormal y excesiva o bien síncrona, y por tanto, es el escenario perfecto para este tipo de análisis al tiempo que presenta un gran interés tanto desde el punto de vista clínico como de investigación. Los resultados manifiestan alteraciones especificas en la conectividad y un cambio en la topología de las redes en cerebros epilépticos, desplazando la importancia del ‘foco’ a la ‘red’, enfoque que va adquiriendo relevancia en las investigaciones recientes sobre epilepsia. ABSTRACT There are about 1014 neuronal synapses in the human brain. This huge number of connections provides the substrate for neuronal ensembles to become transiently synchronized, producing the emergence of cognitive functions such as perception, learning or thinking. Understanding the complex brain network organization on the basis of neuroimaging data represents one of the most important and exciting challenges for systems neuroscience. Several measures have been recently proposed to evaluate at various scales (single cells, cortical columns, or brain areas) how the different parts of the brain communicate. We can classify them, according to their symmetry, into two groups: symmetric measures, such as correlation, coherence or phase synchronization indexes, evaluate functional connectivity (FC); and on the other hand, the asymmetric ones, such as Granger causality or transfer entropy, are able to detect effective connectivity (EC) revealing the direction of the interaction. In modern neurosciences, the interest in functional brain networks has increased strongly with the onset of new algorithms to study interdependence between time series, the advent of modern complex network theory and the introduction of powerful techniques to record neurophysiological data, such as magnetoencephalography (MEG). However, when analyzing neurophysiological data with this approach several questions arise. In this thesis, I intend to tackle some of the practical open problems in the field. First of all, the increase in the number of time series analysis algorithms to study brain FC/EC, along with their mathematical complexity, creates the necessity of arranging them into a single, unified toolbox that allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of them. I developed such a toolbox for this aim, it is named HERMES (http://hermes.ctb.upm.es), and encompasses several of the most common indexes for the assessment of FC and EC running for MatlabR environment. I believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis and will entail a great value for the scientific community. The second important practical issue tackled in this thesis is the evaluation of the sensitivity to deep brain sources of two different MEG sensors: planar gradiometers and magnetometers, in combination with the related methodological approach, using two phase synchronization indexes: phase locking value (PLV) y phase lag index (PLI), the latter one being less sensitive to volume conduction effect. Thus, I compared their performance when studying brain networks, obtaining that magnetometer sensors and PLV presented higher artificial values as compared with planar gradiometers and PLI respectively. However, when it came to characterize epileptic networks it was the PLV which gives better results, as PLI FC networks where very sparse. Complex network analysis has provided new concepts which improved characterization of interacting dynamical systems. With this background, networks could be considered composed of nodes, symbolizing systems, whose interactions with each other are represented by edges. A growing number of network measures is been applied in network analysis. However, there is theoretical and empirical evidence that many of these indexes are strongly correlated with each other. Therefore, in this thesis I reduced them to a small set, which could more efficiently characterize networks. Within this framework, selecting an appropriate threshold to decide whether a certain connectivity value of the FC matrix is significant and should be included in the network analysis becomes a crucial step, in this thesis, I used the surrogate data tests to make an individual data-driven evaluation of each of the edges significance and confirmed more accurate results than when just setting to a fixed value the density of connections. All these methodologies were applied to the study of epilepsy, analysing resting state MEG functional networks, in two groups of epileptic patients (generalized and focal epilepsy) that were compared to matching control subjects. Epilepsy is one of the most common neurological disorders, with more than 55 million people affected worldwide, characterized by its predisposition to generate epileptic seizures of abnormal excessive or synchronous neuronal activity, and thus, this scenario and analysis, present a great interest from both the clinical and the research perspective. Results revealed specific disruptions in connectivity and network topology and evidenced that networks’ topology is changed in epileptic brains, supporting the shift from ‘focus’ to ‘networks’ which is gaining importance in modern epilepsy research.

Importance of canopy porosity into vineyard and the relationship with the grape maturity. Digital estimation method

In warm and dry climates, the use of porous systems should be required in order to allow a better leaf distribution inside the plant, causing more space in the clusters area and enhancing determined physiological processes so in the leaf (photosynthesis, v entilation, transpiration) as in berry (growth and maturation). Plant geometry indexes, yield and must composition have been studied in three different systems: sprawl with 12 shoots/m (S1); sprawl system with 18 shoots/m (S2) and vertical positioned syste m or VSP with 12 shoots/m (VSP1). Total leaf area increases as the crop load does, whoever surface area depends on to two factors: crop load and the training system (VSP vs. sprawl), which can provide differences in leaf exposure efficiencies. The main objective of this study was to validate digital photography measurements used to compare porosity differences among treatments and, as they affect plant microclimate and, therefore, yield and berry quality. Also, all previous studied indexes (LAI, SA, SFEr) tended to overestimate the relationship between exposed leaf surface and porosity of each treatment, but the use of digital method proved to be an effective tool in order to assess canopy porosity. Results showed that not positioned and free systems (sprawl) scored between 25- 50% more porosity in the clusters area than the fixed vertical system (VSP), which resulted in a better plant microclimate for test conditions, mainly by improving the exposure of internal clusters and internal canopy ventilation. On the other hand, higher crop load treatment (S2) showed a real increase in yield (16%) without any relevant change into must composition, even improving total anthocyanin content into berry during ripening

Importance of canopy porosity into vineyard and the relationship with the grape maturity. Digital estimation method

In warm and dry climates, the use of porous systems should be required in order to allow a better leaf distribution inside the plant, causing more space in the clusters area and enhancing determined physiological processes so in the leaf (photosynthesis, ventilation, transpiration) as in berry (growth and maturation). Plant geometry indexes, yield and must composition have been studied in three different systems: sprawl with 12 shoots/m (S1); sprawl system with 18 shoots/m (S2) and vertical positioned system or VSP with 12 shoots/m (VSP1). Total leaf area increases as the crop load does, whoever surface area depends on to two factors: crop load and the training system (VSP vs . sprawl), which can provide differences in leaf exposure efficiencies. The main objective of this study was to validate digital photography measurements used to compare porosity differences among treatments and, as they affect plant microclimate and, therefore, yield and berry quality. Also, all previous studied indexes (LAI, SA, SFEr) tended to overestimate the relationship between exposed leaf surface and porosity of each treatment, but the use of digital method proved to be an effective tool in order to assess canopy porosity. Results showed that not positioned and free systems (sprawl) scored between 25 - 50% more porosity in the clusters area than the fixed vertical system (VSP), which resulted in a better plant microclimate for test conditions, mainly by improving the exposure of internal clusters and internal canopy ventilation. On the other hand, higher crop load treatment (S2) showed a real increase in yield (16%) without any relevant change into must composition, even improving total anthocyanin content into berry during ripening

Membrane Systems Working with the PFactor: Best Strategy to Solve Complex Problems.

A membrane system is a massive parallel system, which is inspired by the living cells when processing information. As a part of unconventional computing, membrane systems are proven to be effective in solving complex problems. A new factor is introduced. This factor can decide whether a technique is worthwhile being used or not. The use of this factor provides the best chances for selecting the strategy for the rules application phase. Referring to the “best” is in reference to the one that reduces execution time within the membrane system. A pre-analysis of the membrane system determines the P-factor, which in return advises the optimal strategy to use. In particular, this paper compares the use of two strategies based on the P-factor and provides results upon the application of them. The paper concludes that the P-factor is an effective indicator for choosing the right strategy to implement the rules application phase in membrane systems.

Review and comparison of effective delayed neutron fraction calculation methods with Monte Carlo codes

The calculation of the effective delayed neutron fraction, beff , with Monte Carlo codes is a complex task due to the requirement of properly considering the adjoint weighting of delayed neutrons. Nevertheless, several techniques have been proposed to circumvent this difficulty and obtain accurate Monte Carlo results for beff without the need of explicitly determining the adjoint flux. In this paper, we make a review of some of these techniques; namely we have analyzed two variants of what we call the k-eigenvalue technique and other techniques based on different interpretations of the physical meaning of the adjoint weighting. To test the validity of all these techniques we have implemented them with the MCNPX code and we have benchmarked them against a range of critical and subcritical systems for which either experimental or deterministic values of beff are available. Furthermore, several nuclear data libraries have been used in order to assess the impact of the uncertainty in nuclear data in the calculated value of beff .

Computer simulation of random parckings for self-similar particle size distributions in soil and granular materials: porosity and pore size distribution

A 2D computer simulation method of random packings is applied to sets of particles generated by a self-similar uniparametric model for particle size distributions (PSDs) in granular media. The parameter p which controls the model is the proportion of mass of particles corresponding to the left half of the normalized size interval [0,1]. First the influence on the total porosity of the parameter p is analyzed and interpreted. It is shown that such parameter, and the fractal exponent of the associated power scaling, are efficient packing parameters, but this last one is not in the way predicted in a former published work addressing an analogous research in artificial granular materials. The total porosity reaches the minimum value for p = 0.6. Limited information on the pore size distribution is obtained from the packing simulations and by means of morphological analysis methods. Results show that the range of pore sizes increases for decreasing values of p showing also different shape in the volume pore size distribution. Further research including simulations with a greater number of particles and image resolution are required to obtain finer results on the hierarchical structure of pore space.

Effective inhibition of influenza virus production in cultured cells by external guide sequences and ribonuclease P

The polymerase (PB2) and nucleocapsid (NP) genes encoded by the genome of influenza virus are essential for replication of the virus. When synthetic genes that express RNAs for external guide sequences targeted to the mRNAs of the PB2 and NP genes are stably incorporated into mouse cells in tissue culture, infection of these cells with influenza virus is nonproductive. Endogenous RNase P cleaves the targeted influenza virus mRNAs when they are in a complex with the external guide sequences. Targeting two different mRNAs simultaneously inhibits viral particle production more efficiently than does targeting only one mRNA.

Heat shock fusion proteins as vehicles for antigen delivery into the major histocompatibility complex class I presentation pathway

Mice immunized with heat shock proteins (hsps) isolated from mouse tumor cells (donor cells) produce CD8 cytotoxic T lymphocytes (CTL) that recognize donor cell peptides in association with the major histocompatibility complex (MHC) class I proteins of the responding mouse. The CTL are induced apparently because peptides noncovalently associated with the isolated hsp molecules can enter the MHC class I antigen processing pathway of professional antigen-presenting cells. Using a recombinant heat shock fusion protein with a large fragment of ovalbumin covalently linked to mycobacterial hsp70, we show here that when the soluble fusion protein was injected without adjuvant into H-2b mice, CTL were produced that recognized an ovalbumin-derived peptide, SIINFEKL, in association with Kb. The peptide is known to arise from natural processing of ovalbumin in H-2b mouse cells, and CTL from the ovalbumin-hsp70-immunized mice and a highly effective CTL clone (4G3) raised against ovalbumin-expressing EL4 tumor cells (EG7-OVA) were equally effective in terms of the concentration of SIINFEKL required for half-maximal lysis in a CTL assay. The mice were also protected against lethal challenge with ovalbumin-expressing melanoma tumor cells. Because large protein fragments or whole proteins serving as fusion partners can be cleaved into short peptides in the MHC class I processing pathway, hsp fusion proteins of the type described here are promising candidates for vaccines aimed at eliciting CD8 CTL in populations of MHC-disparate individuals.

In vitro cloning of complex mixtures of DNA on microbeads: Physical separation of differentially expressed cDNAs

We describe a method for cloning nucleic acid molecules onto the surfaces of 5-μm microbeads rather than in biological hosts. A unique tag sequence is attached to each molecule, and the tagged library is amplified. Unique tagging of the molecules is achieved by sampling a small fraction (1%) of a very large repertoire of tag sequences. The resulting library is hybridized to microbeads that each carry ≈106 strands complementary to one of the tags. About 105 copies of each molecule are collected on each microbead. Because such clones are segregated on microbeads, they can be operated on simultaneously and then assayed separately. To demonstrate the utility of this approach, we show how to label and extract microbeads bearing clones differentially expressed between two libraries by using a fluorescence-activated cell sorter (FACS). Because no prior information about the cloned molecules is required, this process is obviously useful where sequence databases are incomplete or nonexistent. More importantly, the process also permits the isolation of clones that are expressed only in given tissues or that are differentially expressed between normal and diseased states. Such clones then may be spotted on much more cost-effective, tissue- or disease-directed, low-density planar microarrays.

Nonlinear indicial response of complex nonstationary oscillations as pulmonary hypertension responding to step hypoxia

This paper is devoted to the quantization of the degree of nonlinearity of the relationship between two biological variables when one of the variables is a complex nonstationary oscillatory signal. An example of the situation is the indicial responses of pulmonary blood pressure (P) to step changes of oxygen tension (ΔpO2) in the breathing gas. For a step change of ΔpO2 beginning at time t1, the pulmonary blood pressure is a nonlinear function of time and ΔpO2, which can be written as P(t-t1 | ΔpO2). An effective method does not exist to examine the nonlinear function P(t-t1 | ΔpO2). A systematic approach is proposed here. The definitions of mean trends and oscillations about the means are the keys. With these keys a practical method of calculation is devised. We fit the mean trends of blood pressure with analytic functions of time, whose nonlinearity with respect to the oxygen level is clarified here. The associated oscillations about the mean can be transformed into Hilbert spectrum. An integration of the square of the Hilbert spectrum over frequency yields a measure of oscillatory energy, which is also a function of time, whose mean trends can be expressed by analytic functions. The degree of nonlinearity of the oscillatory energy with respect to the oxygen level also is clarified here. Theoretical extension of the experimental nonlinear indicial functions to arbitrary history of hypoxia is proposed. Application of the results to tissue remodeling and tissue engineering of blood vessels is discussed.

Characterization of complex mineral assemblages: Implications for contaminant transport and environmental remediation

Surface reactive phases of soils and aquifers, comprised of phyllosilicate and metal oxohydroxide minerals along with humic substances, play a critical role in the regulation of contaminant fate and transport. Much of our knowledge concerning contaminant-mineral interactions at the molecular level, however, is derived from extensive experimentation on model mineral systems. Although these investigations have provided a foundation for understanding reactive surface functional groups on individual mineral phases, the information cannot be readily extrapolated to complex mineral assemblages in natural systems. Recent studies have elucidated the role of less abundant mineral and organic substrates as important surface chemical modifiers and have demonstrated complex coupling of reactivity between permanent-charge phyllosilicates and variable-charge Fe-oxohydroxide phases. Surface chemical modifiers were observed to control colloid generation and transport processes in surface and subsurface environments as well as the transport of solutes and ionic tracers. The surface charging mechanisms operative in the complex mineral assemblages cannot be predicted based on bulk mineralogy or by considering surface reactivity of less abundant mineral phases based on results from model systems. The fragile nature of mineral assemblages isolated from natural systems requires novel techniques and experimental approaches for investigating their surface chemistry and reactivity free of artifacts. A complete understanding of the surface chemistry of complex mineral assemblages is prerequisite to accurately assessing environmental and human health risks of contaminants or in designing environmentally sound, cost-effective chemical and biological remediation strategies.

Transloading of tumor cells with foreign major histocompatibility complex class I peptide ligand: a novel general strategy for the generation of potent cancer vaccines.

The major hurdle to be cleared in active immunotherapy of cancer is the poor immunogenicity of cancer cells. In previous attempts to overcome this problem, whole tumor cells have been used as vaccines, either admixed with adjuvant(s) or genetically engineered to express nonself proteins or immunomodulatory factors before application. We have developed a novel approach to generate an immunogeneic, highly effective vaccine: major histocompatibility complex (MHC) class I-positive cancer cells are administered together with MHC class I-matched peptide ligands of foreign, nonself origin, generated by a procedure we term transloading. Murine tumor lines of the H2-Kd or the H2-Db haplotype, melanoma M-3 and B16-F10, respectively, as well as colon carcinoma CT-26 (H2-Kd), were transloaded with MHC-matched influenza virus-derived peptides and applied as irradiated vaccines. Mice bearing a deposit of live M-3 melanoma cells were efficiently cured by this treatment. In the CT-26 colon carcinoma and the B16-F10 melanoma, high efficacies were obtained against tumor challenge, suggesting the universal applicability of this new type of vaccine. With foreign peptide ligands adapted to the requirements of a desired MHC class I haplotype, this concept may be used for the treatment of human cancers.

Passive immunotherapy for retroviral disease: influence of major histocompatibility complex type and T-cell responsiveness.

Administration of virus-specific antibodies is known to be an effective early treatment for some viral infections. Such immunotherapy probably acts by antibody-mediated neutralization of viral infectivity and is often thought to function independently of T-cell-mediated immune responses. In the present experiments, we studied passive antibody therapy using Friend murine leukemia virus complex as a model for an immunosuppressive retroviral disease in adult mice. The results showed that antibody therapy could induce recovery from a well-established retroviral infection. However, the success of therapy was dependent on the presence of both CD4+ and CD8+ T lymphocytes. Thus, cell-mediated responses were required for recovery from infection even in the presence of therapeutic levels of antibody. The major histocompatibility type of the mice was also an important factor determining the relative success of antibody therapy in this system, but it was less critical for low-dose than for high-dose infections. Our results imply that limited T-cell responsiveness as dictated by major histocompatibility genes and/or stage of disease may have contributed to previous immunotherapy failures in AIDS patients. Possible strategies to improve the efficacy of future therapies are discussed.

Upregulation of class I major histocompatibility complex antigens by interferon gamma is necessary for T-cell-mediated elimination of recombinant adenovirus-infected hepatocytes in vivo.

Recombinant adenoviruses are attractive vehicles for liver-directed gene therapy because of the high efficiency with which they transfer genes to hepatocytes in vivo. First generation recombinant adenoviruses deleted of E1 sequences also express recombinant and early and late viral genes, which lead to development of destructive cellular immune responses. Previous studies indicated that class I major histocompatibility complex (MHC)-restricted cytotoxic T lymphocytes (CTLs) play a major role in eliminating virus-infected cells. The present studies utilize mouse models to evaluate the role of T-helper cells in the primary response to adenovirus-mediated gene transfer to the liver. In vivo ablation of CD4+ cells or interferon gamma (IFN-gamma) was sufficient to prevent the elimination of adenovirus-transduced hepatocytes, despite the induction of a measurable CTL response. Mobilization of an effective TH1 response as measured by in vitro proliferation assays was associated with substantial upregulation of MHC class I expression, an effect that was prevented in IFN-gamma-deficient animals. These results suggest that elimination of virus-infected hepatocytes in a primary exposure to recombinant adenovirus requires both induction of antigen-specific CTLs as well as sensitization of the target cell by TH1-mediated activation of MHC class I expression.