Análisis de autoreactividad de anticuerpos leucémicos soportado por estrategias de Inteligencia Artificial

184 p.Los antígenos son moléculas externas reconocidas por el organismo de variada estructura y naturaleza. El sistema inmune ha desarrollado técnicas de reconocimiento para estos agentes patógenos, representando diferentes mecanismos de defensa contra una posible infección, siendo los anticuerpos los responsables de esta detección. Predecir qué anticuerpo reconocerá a un antígeno, o estimar a nivel cualitativo la intensidad de la interacción que se producirá, es una tarea ardua y compleja, representando un gran desafío en el área inmunológica. Debido a que los antígenos pueden ser distintos tipos de moléculas, y tener procedencia en diferentes patógenos, la forma en la cual un anticuerpo reconoce un conjunto de antígenos con diversas intensidades de interacción, es una pregunta que se ha abordado desde diferentes perspectivas. Por otra parte, el organismo ha desarrollado estrategias para reconocer moléculas externas de aquellas propias. Esto evita que se genere una respuesta inmune sobre tejidos en el organismo. Las moléculas propias del organismo que desencadenan esta respuesta son denominadas auto antígenos, y al proceso de presentar defensas contra estas moléculas se le denomina auto reactividad. El análisis de auto antígenos es de gran relevancia, tanto para el estudio de enfermedades auto inmunes, como para enfermedades relacionadas a células propias del organismo. En el caso de la leucemia, un tipo de cáncer que afecta a células del tejido sanguíneo, el estudio de la auto reactividad y la interacción entre auto antígenos y anticuerpos es de gran relevancia para el diseño y propuestas que permitan diagnosticar y tratar esta enfermedad. Gran parte de los estudios de interacción entre auto antígenos y anticuerpos se han realizado utilizando técnicas experimentales. No obstante, diversos enfoques in-silico han sido desarrollados empleando diferentes herramientas computacionales como docking o simulación molecular para cálculos de energía libre y visualización de interacciones. Pese a su gran utilidad, estas técnicas poseen un alto costo asociados a la necesidad de material experimental, necesidad de poseer estructuras definidas o modelos confiables, elevados tiempos de simulación, entre otros. De esta forma la aplicación de técnicas de machine learning y diversos métodos de codificación representan una alternativa potente al problema de reconocimiento de interacción entre proteína-proteína, particularmente, a secuencias de auto antígenos y anticuerpos de leucemia. A partir de la información de interacciones entre 45 secuencias de cadena pesada de anticuerpos y cerca de 8000 secuencias de auto antígenos, Se diseñó e implemento un sistema predictivo ensamblado cualitativo del nivel de intensidad de la interacción entre auto antígenos y cadenas pesadas de anticuerpos. Como estrategias de entrenamiento de modelos predictivos, se combinaron variados métodos de representación de proteínas, principalmente Natural Language Processing y propiedades fisicoquímicas, con diferentes algoritmos de aprendizaje supervisado logrando un predictor ensamblado con un rendimiento del 81% de accuracy. Se aplicaron diferentes estrategias de validación que permiten demostrar la robustez del sistema predictivo propuesto, incluyendo sistemas de validación cruzada y métodos propios basados en estrategias Leave One Antibody Out. Adicionalmente, se diseñó e implemento un conjunto de colecciones de moléculas inmunológicas integradas en un único sistema de base de datos, al cual acoplado a una estrategia de clasificación filogenética, se diseña e implementa una estrategia de clasificación de secuencias de autoantígenos basado en propiedades descriptivas, funcionales y componentes filogenéticos. La combinación del conjunto de colecciones con el sistema de clasificación, en conjunto con el sistema ensamblado predictivo, facilita el diseño de estrategias de identificación de secuencias autoantígenos y su evaluación contra anticuerpos leucémicos, brindando los soportes iniciales para herramientas de diseño y descubrimiento de antígenos/anticuerpos que cumplan con características relevantes para el problema de la leucemia, denotando la usabilidad de métodos computacionales en problemas complejos de la ingeniería médica. // ABSTRACT: Antigens are external molecules of varied structures and nature recognized by the body. The immune system has developed recognition techniques for these pathogens, representing different defense mechanisms against possible infection, the antibodies responsible for this detection. Predicting which antibody will recognize an antigen or estimating the intensity of the interaction that will occur at a qualitative level is an arduous and complex task, representing a significant challenge in the immunological area. Because antigens can be different types of molecules and have origins in various pathogens, how an antibody recognizes a set of antigens with varying intensities of interaction is a question that has been approached from different perspectives. On the other hand, the organism has developed strategies to recognize external molecules on its own. This prevents an immune response from being generated on tissues in the body. The body’s own molecules that trigger this response are called self-antigens, and the process of presenting defenses against these molecules is called self-reactivity. The analysis of self-antigens is of great relevance, both for studying autoimmune diseases and for diseases related to the body’s own cells. In leukemia, a type of cancer that affects cells of the blood tissue, the study of self-reactivity and the interaction between self-antigens and antibodies is of great relevance for the design of proposals that allow the diagnosis and treatment of this disease. Much of the interaction studies between self-antigens and antibodies have been carried out using experimental techniques. However, various in-silico approaches have been developed using different computational tools such as docking or molecular simulation for free energy calculations and interactive visualization. Despite their great utility, these techniques have a high cost associated with the need for experimental material, the need to have defined structures or reliable models, high simulation times, among others. In this way, the application of machine learning techniques and various coding methods represent a powerful alternative to the problem of protein-protein interaction recognition, particularly to leukemia self-antigen and antibody sequences. From the information of interactions between 45 sequences of antibodies heavy chain and about 8000 sequences of self-antigens, a qualitative assembled predictive system for the level of intensity of the interaction between self-antigens and heavy chains of antibodies was designed and implemented. As predictive model training strategies, various protein representation methods were combined, mainly Natural Language Processing and physicochemical properties, with different supervised learning algorithms, achieving an assembled predictor with a performance of 81% accuracy. Different validation strategies were applied to demonstrate the robustness of the proposed predictive system, including cross-validation systems and proprietary methods based on Leave One Antibody Out strategies. Additionally, a set of collections of immunological molecules integrated into a single database system was designed and implemented. Coupled with a phylogenetic classification strategy, a method for classifying self-antigen sequences based on descriptive properties was designed and implemented. This method uses different functional properties and phylogenetic components to estimate the relation of new sequences with the set of self-antigen sequences. The combination of the group of collections with the classification system, in association with the assembled predictive system, facilitates the design of strategies for the identification of selfantigen sequences and their evaluation against leukemic antibodies, providing the initial supports for tools of creation and discovery of antigens/antibodies that meet relevant characteristics for the leukemia problem, denoting the usability of computational methods in complex issues of medical engineering

Aplicaciones de estructuras de grafos y aprendizaje profundo a sistemas de clasificación de interacción antígeno-anticuerpo

72 p.Las interacciones proteína-proteína son de real importancia para la ingeniería de proteínas debido a que forman parte esencial en la mayoría de los procesos moleculares. Un caso particular es la interacción antígeno-anticuerpo, la cual cumple con el rol de inhibir o neutralizar agentes patógenos que afectan negativamente la homeostasis normal del cuerpo. Conocer el funcionamiento específico de un anticuerpo es de gran interés en áreas como la medicina y la farmacología, ya que facilita el diseño de vacunas y medicamentos. Variados métodos experimentales se han desarrollado con el fin de estudiar las interacciones proteína-proteína. Algunos de los ejemplos clásicos corresponden a los microarrays de ADN y proteína, la espectroscopia de masas (MS) y la letalidad sintética. Sin embargo, estos métodos se caracterizan por tener un alto costo de producción y tiempo de desarrollo, ser susceptibles al error humano, y muchas veces, requieren de un elevado conocimiento. Para solventar este problema, cada vez se aplican más técnicas basadas en Machine Learning y Deep Learning, como es el caso de AlphaFold y su capacidad de predecir la estructura secundaria. Sin embargo, la rama de la inteligencia artificial aún debe ser más estudiada y aplicada en la investigación científica. Con base en esto, se realizó una investigación con el fin de predecir la interacción antígeno-anticuerpo por medio de Graph Neural Network. Para lograr esto, las proteínas se representaron por medios de estructuras de grafos, en donde los nodos correspondían a los residuos de las proteínas, mientras que las aristas a las distancias euclidianas entre aminoácidos. Además, se contó con un clasificador de interacción para cada complejo. En general, se obtuvo un rendimiento alrededor del 0,51 y se planteó una serie de puntos a tratar en futuros trabajos para el perfeccionamiento de los modelos, los cuales tienen que ver con arquitecturas de redes neuronales, representación de grafos, métodos de codificación y predicción de complejos proteicos. Demostrando que, a pesar de que los resultados no fueron los esperados, el camino por delante es extenso y queda un largo desarrollo por realizar, con el fin de llegar a elaborar sistemas predictivos en base a esta arquitectura de deep learning. // ABSTRACT: Protein-protein interactions are significant for protein engineering because they are essential to most molecular processes. A particular case is an antigen-antibody interaction, which fulfills the role of inhibiting or neutralizing pathogenic agents that negatively affect the normal homeostasis of the body. Knowing the specific functioning of an antibody is of great interest in areas such as medicine and pharmacology since it facilitates the design of vaccines and drugs. Various experimental methods have been developed to study protein-protein interactions. Some classic examples are DNA and protein microarrays, mass spectroscopy (MS), and synthetic lethality. However, these methods are characterized by high production cost and development time, being susceptible to human error, and often requiring a high level of knowledge. To solve this problem, more and more techniques based on Machine Learning and Deep Learning are being applied, such as AlphaFold and its ability to predict secondary structure. However, the branch of artificial intelligence still needs to be more studied and used in scientific research. Based on this, an investigation was carried out to predict the antigenantibody interaction using the Graph Neural Network. Proteins were represented by employing graph structures, where nodes corresponded to protein residues while edges to Euclidean distances between amino acids. In addition, there was an interaction classifier for each complex. In general, a performance of around 0.51 was obtained, and a series of points were raised to be dealt with in future Works to improve the models, which have to do with neural network architectures, graph representation, coding methods, and prediction of protein complexes. Even though the results were not as expected, the road ahead is long, and there is a long development to be done to develop predictive systems based on this deep learning architecture

Higgs Low-Energy Theorem (and its corrections) in Composite Models

The Higgs low-energy theorem gives a simple and elegant way to estimate the couplings of the Higgs boson to massless gluons and photons induced by loops of heavy particles. We extend this theorem to take into account possible nonlinear Higgs interactions resulting from a strong dynamics at the origin of the breaking of the electroweak symmetry. We show that, while it approximates with an accuracy of order a few percents single Higgs production, it receives corrections of order 50% for double Higgs production. A full one-loop computation of the gg->hh cross section is explicitly performed in MCHM5, the minimal composite Higgs model based on the SO(5)/SO(4) coset with the Standard Model fermions embedded into the fundamental representation of SO(5). In particular we take into account the contributions of all fermionic resonances, which give sizeable (negative) corrections to the result obtained considering only the Higgs nonlinearities. Constraints from electroweak precision and flavor data on the top partners are analyzed in detail, as well as direct searches at the LHC for these new fermions called to play a crucial role in the electroweak symmetry breaking dynamics.Comment: 30 pages + appendices and references, 12 figures. v2: discussion of flavor constraints improved; references added; electroweak fit updated, results unchanged. Matches published versio

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

SummaryBackground The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation

“Still good life”: On the value of reuse and distributive labor in “depleted” rural Maine

This article explores the production of wealth through distributive labor in Maine\u27s secondhand economy. While reuse is often associated with economic disadvantage, our research complicates that perspective. The labor required to reclaim, repair, redistribute, and reuse secondhand goods provides much more than a means of living in places left behind by international capitalism, but the value generated by this work is persistently discounted by dominant economic logics. On the basis of semistructured interviews, participant observation, and statewide surveys with reuse market participants in Maine, we find that the relational value of reuse, produced through caring, flexible, distributive labor, is especially significant. We argue that paying attention to the practices, politics, and value of distribution is critical for understanding wealth in communities perceived to have been left behind by global capitalist systems, particularly as wage labor opportunities and natural resources grow increasingly scarce

Use of an orthovoltage X-ray treatment unit as a radiation research system in a small-animal cancer model

<p>Abstract</p> <p>Background</p> <p>We explore the use of a clinical orthovoltage X-ray treatment unit as a small-animal radiation therapy system in a tumoral model of cervical cancer.</p> <p>Methods</p> <p>Nude mice were subcutaneously inoculated with 5 × 10⁶HeLa cells in both lower limbs. When tumor volume approximated 200 mm³treatment was initiated. Animals received four 2 mg/kg intraperitoneal cycles (1/week) of cisplatin and/or 6.25 mg/kg of gemcitabine, concomitant with radiotherapy. Tumors were exposed to 2.5 Gy/day nominal surface doses (20 days) of 150 kV X-rays. Lead collimators with circular apertures (0.5 to 1.5 cm diameter) were manufactured and mounted on the applicator cone to restrict the X-ray beam onto tumors. X-ray penetration and conformality were evaluated by measuring dose at the surface and behind the tumor lobe by using HS GafChromic film. Relative changes in tumor volume (RTV) and a clonogenic assay were used to evaluate the therapeutic response of the tumor, and relative weight loss was used to assess toxicity of the treatments.</p> <p>Results</p> <p>No measurable dose was delivered outside of the collimator apertures. The analysis suggests that dose inhomogeneities in the tumor reach up to ± 11.5% around the mean tumor dose value, which was estimated as 2.2 Gy/day. Evaluation of the RTV showed a significant reduction of the tumor volume as consequence of the chemoradiotherapy treatment; results also show that toxicity was well tolerated by the animals.</p> <p>Conclusion</p> <p>Results and procedures described in the present work have shown the usefulness and convenience of the orthovoltage X-ray system for animal model radiotherapy protocols.</p

The Actin-Binding Protein Capulet Genetically Interacts with the Microtubule Motor Kinesin to Maintain Neuronal Dendrite Homeostasis

BACKGROUND: Neurons require precise cytoskeletal regulation within neurites, containing microtubule tracks for cargo transport in axons and dendrites or within synapses containing organized actin. Due to the unique architecture and specialized function of neurons, neurons are particularly susceptible to perturbation of the cytoskeleton. Numerous actin-binding proteins help maintain proper cytoskeletal regulation. METHODOLOGY/PRINCIPAL FINDINGS: From a Drosophila forward genetic screen, we identified a mutation in capulet--encoding a conserved actin-binding protein--that causes abnormal aggregates of actin within dendrites. Through interaction studies, we demonstrate that simultaneous genetic inactivation of capulet and kinesin heavy chain, a microtubule motor protein, produces elongate cofilin-actin rods within dendrites but not axons. These rods resemble actin-rich structures induced in both mammalian neurodegenerative and Drosophila Alzheimer's models, but have not previously been identified by loss of function mutations in vivo. We further demonstrate that mitochondria, which are transported by Kinesin, have impaired distribution along dendrites in a capulet mutant. While Capulet and Cofilin may biochemically cooperate in certain circumstances, in neuronal dendrites they genetically antagonize each other. CONCLUSIONS/SIGNIFICANCE: The present study is the first molecularly defined loss of function demonstration of actin-cofilin rods in vivo. This study suggests that simultaneous, seemingly minor perturbations in neuronal dendrites can synergize producing severe abnormalities affecting actin, microtubules and mitochondria/energy availability in dendrites. Additionally, as >90% of Alzheimer's and Parkinson's cases are sporadic this study suggests mechanisms by which multiple mutations together may contribute to neurodegeneration instead of reliance on single mutations to produce disease

Airway Microbiota and Pathogen Abundance in Age-Stratified Cystic Fibrosis Patients

Bacterial communities in the airways of cystic fibrosis (CF) patients are, as in other ecological niches, influenced by autogenic and allogenic factors. However, our understanding of microbial colonization in younger versus older CF airways and the association with pulmonary function is rudimentary at best. Using a phylogenetic microarray, we examine the airway microbiota in age stratified CF patients ranging from neonates (9 months) to adults (72 years). From a cohort of clinically stable patients, we demonstrate that older CF patients who exhibit poorer pulmonary function possess more uneven, phylogenetically-clustered airway communities, compared to younger patients. Using longitudinal samples collected form a subset of these patients a pattern of initial bacterial community diversification was observed in younger patients compared with a progressive loss of diversity over time in older patients. We describe in detail the distinct bacterial community profiles associated with young and old CF patients with a particular focus on the differences between respective “early” and “late” colonizing organisms. Finally we assess the influence of Cystic Fibrosis Transmembrane Regulator (CFTR) mutation on bacterial abundance and identify genotype-specific communities involving members of the Pseudomonadaceae, Xanthomonadaceae, Moraxellaceae and Enterobacteriaceae amongst others. Data presented here provides insights into the CF airway microbiota, including initial diversification events in younger patients and establishment of specialized communities of pathogens associated with poor pulmonary function in older patient populations