Gene Expression Profiling in Limb-Girdle Muscular Dystrophy 2A

Limb-girdle muscular dystrophy type 2A (LGMD2A) is a recessive genetic disorder caused by mutations in calpain 3 (CAPN3). Calpain 3 plays different roles in muscular cells, but little is known about its functions or in vivo substrates. The aim of this study was to identify the genes showing an altered expression in LGMD2A patients and the possible pathways they are implicated in. Ten muscle samples from LGMD2A patients with in which molecular diagnosis was ascertained were investigated using array technology to analyze gene expression profiling as compared to ten normal muscle samples. Upregulated genes were mostly those related to extracellular matrix (different collagens), cell adhesion (fibronectin), muscle development (myosins and melusin) and signal transduction. It is therefore suggested that different proteins located or participating in the costameric region are implicated in processes regulated by calpain 3 during skeletal muscle development. Genes participating in the ubiquitin proteasome degradation pathway were found to be deregulated in LGMD2A patients, suggesting that regulation of this pathway may be under the control of calpain 3 activity. As frizzled-related protein (FRZB) is upregulated in LGMD2A muscle samples, it could be hypothesized that β-catenin regulation is also altered at the Wnt signaling pathway, leading to an incorrect myogenesis. Conversely, expression of most transcription factor genes was downregulated (MYC, FOS and EGR1). Finally, the upregulation of IL-32 and immunoglobulin genes may induce the eosinophil chemoattraction explaining the inflammatory findings observed in presymptomatic stages. The obtained results try to shed some light on identification of novel therapeutic targets for limb-girdle muscular dystrophies

Image Processing Challenges in the Creation of Spatiotemporal Gene Expression Atlases of Developing Embryos

To properly understand and model animal embryogenesis it is crucial to obtain detailed measurements, both in time and space, about their gene expression domains and cell dynamics. Such challenge has been confronted in recent years by a surge of atlases which integrate a statistically relevant number of different individuals to get robust, complete information about their spatiotemporal locations of gene patterns. This paper will discuss the fundamental image analysis strategies required to build such models and the most common problems found along the way. We also discuss the main challenges and future goals in the field.

Image Processing Challenges in the Creation of Spatiotemporal Gene Expression Atlases of Developing Embryos

To properly understand and model animal embryogenesis it is crucial to obtain detailed measurements, both in time and space, about their gene expression domains and cell dynamics. Such challenge has been confronted in recent years by a surge of atlases which integrate a statistically relevant number of different individuals to get robust, complete information about their spatiotemporal locations of gene patterns. This paper will discuss the fundamental image analysis strategies required to build such models and the most common problems found along the way. We also discuss the main challenges and future goals in the field.

Computational methods to create and analyze a digital gene expression atlas of embryo development from microscopy images

Abstract The creation of atlases, or digital models where information from different subjects can be combined, is a field of increasing interest in biomedical imaging. When a single image does not contain enough information to appropriately describe the organism under study, it is then necessary to acquire images of several individuals, each of them containing complementary data with respect to the rest of the components in the cohort. This approach allows creating digital prototypes, ranging from anatomical atlases of human patients and organs, obtained for instance from Magnetic Resonance Imaging, to gene expression cartographies of embryo development, typically achieved from Light Microscopy. Within such context, in this PhD Thesis we propose, develop and validate new dedicated image processing methodologies that, based on image registration techniques, bring information from multiple individuals into alignment within a single digital atlas model. We also elaborate a dedicated software visualization platform to explore the resulting wealth of multi-dimensional data and novel analysis algo-rithms to automatically mine the generated resource in search of bio¬logical insights. In particular, this work focuses on gene expression data from developing zebrafish embryos imaged at the cellular resolution level with Two-Photon Laser Scanning Microscopy. Disposing of quantitative measurements relating multiple gene expressions to cell position and their evolution in time is a fundamental prerequisite to understand embryogenesis multi-scale processes. However, the number of gene expressions that can be simultaneously stained in one acquisition is limited due to optical and labeling constraints. These limitations motivate the implementation of atlasing strategies that can recreate a virtual gene expression multiplex. The developed computational tools have been tested in two different scenarios. The first one is the early zebrafish embryogenesis where the resulting atlas constitutes a link between the phenotype and the genotype at the cellular level. The second one is the late zebrafish brain where the resulting atlas allows studies relating gene expression to brain regionalization and neurogenesis. The proposed computational frameworks have been adapted to the requirements of both scenarios, such as the integration of partial views of the embryo into a whole embryo model with cellular resolution or the registration of anatom¬ical traits with deformable transformation models non-dependent on any specific labeling. The software implementation of the atlas generation tool (Match-IT) and the visualization platform (Atlas-IT) together with the gene expression atlas resources developed in this Thesis are to be made freely available to the scientific community. Lastly, a novel proof-of-concept experiment integrates for the first time 3D gene expression atlas resources with cell lineages extracted from live embryos, opening up the door to correlate genetic and cellular spatio-temporal dynamics. La creación de atlas, o modelos digitales, donde la información de distintos sujetos puede ser combinada, es un campo de creciente interés en imagen biomédica. Cuando una sola imagen no contiene suficientes datos como para describir apropiadamente el organismo objeto de estudio, se hace necesario adquirir imágenes de varios individuos, cada una de las cuales contiene información complementaria respecto al resto de componentes del grupo. De este modo, es posible crear prototipos digitales, que pueden ir desde atlas anatómicos de órganos y pacientes humanos, adquiridos por ejemplo mediante Resonancia Magnética, hasta cartografías de la expresión genética del desarrollo de embrionario, típicamente adquiridas mediante Microscopía Optica. Dentro de este contexto, en esta Tesis Doctoral se introducen, desarrollan y validan nuevos métodos de procesado de imagen que, basándose en técnicas de registro de imagen, son capaces de alinear imágenes y datos provenientes de múltiples individuos en un solo atlas digital. Además, se ha elaborado una plataforma de visualization específicamente diseñada para explorar la gran cantidad de datos, caracterizados por su multi-dimensionalidad, que resulta de estos métodos. Asimismo, se han propuesto novedosos algoritmos de análisis y minería de datos que permiten inspeccionar automáticamente los atlas generados en busca de conclusiones biológicas significativas. En particular, este trabajo se centra en datos de expresión genética del desarrollo embrionario del pez cebra, adquiridos mediante Microscopía dos fotones con resolución celular. Disponer de medidas cuantitativas que relacionen estas expresiones genéticas con las posiciones celulares y su evolución en el tiempo es un prerrequisito fundamental para comprender los procesos multi-escala característicos de la morfogénesis. Sin embargo, el número de expresiones genéticos que pueden ser simultáneamente etiquetados en una sola adquisición es reducido debido a limitaciones tanto ópticas como del etiquetado. Estas limitaciones requieren la implementación de estrategias de creación de atlas que puedan recrear un multiplexado virtual de expresiones genéticas. Las herramientas computacionales desarrolladas han sido validadas en dos escenarios distintos. El primer escenario es el desarrollo embrionario temprano del pez cebra, donde el atlas resultante permite constituir un vínculo, a nivel celular, entre el fenotipo y el genotipo de este organismo modelo. El segundo escenario corresponde a estadios tardíos del desarrollo del cerebro del pez cebra, donde el atlas resultante permite relacionar expresiones genéticas con la regionalización del cerebro y la formación de neuronas. La plataforma computacional desarrollada ha sido adaptada a los requisitos y retos planteados en ambos escenarios, como la integración, a resolución celular, de vistas parciales dentro de un modelo consistente en un embrión completo, o el alineamiento entre estructuras de referencia anatómica equivalentes, logrado mediante el uso de modelos de transformación deformables que no requieren ningún marcador específico. Está previsto poner a disposición de la comunidad científica tanto la herramienta de generación de atlas (Match-IT), como su plataforma de visualización (Atlas-IT), así como las bases de datos de expresión genética creadas a partir de estas herramientas. Por último, dentro de la presente Tesis Doctoral, se ha incluido una prueba conceptual innovadora que permite integrar los mencionados atlas de expresión genética tridimensionales dentro del linaje celular extraído de una adquisición in vivo de un embrión. Esta prueba conceptual abre la puerta a la posibilidad de correlar, por primera vez, las dinámicas espacio-temporales de genes y células.

Distinct and conserved transcriptomic changes during nematode-induced giant cell development in tomato compared with Arabidopsis: a functional role for gene repression

Root-knot nematodes (RKNs) induce giant cells (GCs) from root vascular cells inside the galls. Accompanying molecular changes as a function of infection time and across different species, and their functional impact, are still poorly understood. Thus, the transcriptomes of tomato galls and laser capture microdissected (LCM) GCs over the course of parasitism were compared with those of Arabidopsis, and functional analysis of a repressed gene was performed. Microarray hybridization with RNA from galls and LCM GCs, infection-reproduction tests and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) transcriptional profiles in susceptible and resistant (Mi-1) lines were performed in tomato. Tomato GC-induced genes include some possibly contributing to the epigenetic control of GC identity. GC-repressed genes are conserved between tomato and Arabidopsis, notably those involved in lignin deposition. However, genes related to the regulation of gene expression diverge, suggesting that diverse transcriptional regulators mediate common responses leading to GC formation in different plant species. TPX1, a cell wall peroxidase specifically involved in lignification, was strongly repressed in GCs/galls, but induced in a nearly isogenic Mi-1 resistant line on nematode infection. TPX1 overexpression in susceptible plants hindered nematode reproduction and GC expansion. Time-course and cross-species comparisons of gall and GC transcriptomes provide novel insights pointing to the relevance of gene repression during RKN establishment.

Azotobacter vinelandii nitrogenase: “Kinetics of nif gene expression and insights into the roles of FdxN and NifQ in FeMo-co biosynthesis”

The Molybdenum-nitrogenase is responsible for most biological nitrogen fixation activity (BNF) in the biosphere. Due to its great agronomical importance, it has been the subject of profound genetic and biochemical studies. The Mo nitrogenase carries at its active site a unique iron-molybdenum cofactor (FeMoco) that consists of an inorganic 7 Fe, 1 Mo, 1 C, 9 S core coordinated to the organic acid homocitrate. Biosynthesis of FeMo-co occurs outside nitrogenase through a complex and highly regulated pathway involving proteins acting as molecular scaffolds, metallocluster carriers or enzymes that provide substrates in appropriate chemical forms. Specific expression regulatory factors tightly control the accumulation levels of all these other components. Insertion of FeMo-co into a P-cluster containing apo-NifDK polypeptide results in nitrogenase reconstitution. Investigation of FeMo-co biosynthesis has uncovered new radical chemistry reactions and new roles for Fe-S clusters in biology.

A digital framework to build, visualize and analyze a gene expression atlas with cellular resolution in zebrafish early embryogenesis

A gene expression atlas is an essential resource to quantify and understand the multiscale processes of embryogenesis in time and space. The automated reconstruction of a prototypic 4D atlas for vertebrate early embryos, using multicolor fluorescence in situ hybridization with nuclear counterstain, requires dedicated computational strategies. To this goal, we designed an original methodological framework implemented in a software tool called Match-IT. With only minimal human supervision, our system is able to gather gene expression patterns observed in different analyzed embryos with phenotypic variability and map them onto a series of common 3D templates over time, creating a 4D atlas. This framework was used to construct an atlas composed of 6 gene expression templates from a cohort of zebrafish early embryos spanning 6 developmental stages from 4 to 6.3 hpf (hours post fertilization). They included 53 specimens, 181,415 detected cell nuclei and the segmentation of 98 gene expression patterns observed in 3D for 9 different genes. In addition, an interactive visualization software, Atlas-IT, was developed to inspect, supervise and analyze the atlas. Match-IT and Atlas-IT, including user manuals, representative datasets and video tutorials, are publicly and freely available online. We also propose computational methods and tools for the quantitative assessment of the gene expression templates at the cellular scale, with the identification, visualization and analysis of coexpression patterns, synexpression groups and their dynamics through developmental stages.