Disentangling function from topology to infer the network properties of disease genes

Background: The topological features of disease genes within interaction networks are the subject of intense study, as they shed light on common mechanisms of pathology and are useful for uncovering additional disease genes. Computational analyses typically try to uncover whether disease genes exhibit distinct network features, as compared to all genes. Results: We demonstrate that the functional composition of disease gene sets is an important confounding factor in these types of analyses. We consider five disease sets and show that while they indeed have distinct topological features, they are also enriched in functions that a priori exhibit distinct network properties. To address this, we develop a computational framework to assess the network properties of disease genes based on a sampling algorithm that generates control gene sets that are functionally similar to the disease set. Using our function-constrained sampling approach, we demonstrate that for most of the topological properties studied, disease genes are more similar to sets of genes with similar functional make-up than they are to randomly selected genes; this suggests that these observed differences in topological properties reflect not only the distinguishing network features of disease genes but also their functional composition. Nevertheless, we also highlight many cases where disease genes have distinct topological properties even when accounting for function. Conclusions: Our approach is an important first step in extracting the residual topological differences in disease genes when accounting for function, and leads to new insights into the network properties of disease genes

Network link prediction by global silencing of indirect correlations

Predicting physical and functional links between cellular components is a fundamental challenge of biology and network science. Yet, correlations, a ubiquitous input for biological link prediction, are affected by both direct and indirect effects, confounding our ability to identify true pairwise interactions. Here we exploit the fundamental properties of dynamical correlations in networks to develop a method to silence indirect effects. The method receives as input the observed correlations between node pairs and uses a matrix transformation to turn the correlation matrix into a highly discriminative silenced matrix, which enhances only the terms associated with direct causal links. Achieving perfect accuracy in model systems, we test the method against empirical data collected for the Escherichia coli regulatory interaction network, showing that it improves on the best preforming link prediction methods. Overall the silencing methodology helps translate the abundant correlation data into valuable local information, with applications ranging from link prediction to inferring the dynamical mechanisms governing biological networks

Disentangling ecological networks in marine microbes

There is a myriad of microorganisms on Earth contributing to global biogeochemical cycles, and their interactions are considered pivotal for ecosystem function. Previous studies have already determined relationships between a limited number of microorganisms. Yet, we still need to understand a large number of interactions to increase our knowledge of complex microbiomes. This is challenging because of the vast number of possible interactions. Thus, microbial interactions still remain barely known to date. Networks are a great tool to handle the vast number of microorganisms and their connections, explore potential microbial interactions, and elucidate patterns of microbial ecosystems. This thesis locates at the intersection of network inference and network analysis. The presented methodology aims to support and advance marine microbial investigations by reducing noise and elucidating patterns in inferred association networks for subsequent biological down-stream analyses. This thesis’s main contribution to marine microbial interactions studies is the development of the program EnDED (Environmentally-Driven Edge Detection), a computational framework to identify environmentally-driven associations inside microbial association networks, inferred from omics datasets. We applied the methodology to a model marine microbial ecosystem at the Blanes Bay Microbial Observatory (BBMO) in the North-Western Mediterranean Sea (ten years of monthly sampling). We also applied the methodology to a dataset compilation covering six global-ocean regions from the surface (3 m) to the deep ocean (down to 4539 m). Thus, our methodology provided a step towards studying the marine microbial distribution in space via the horizontal (ocean regions) and vertical (water column) axes.Hi ha una infinitat de microorganismes a la Terra que contribueixen als cicles biogeoquímics mundials i les seves interaccions es consideren fonamentals pel funcionament dels ecosistemes. Estudis previs ja han determinat les relacions entre un nombre limitat de microorganismes. Tot i això, encara hem d’entendre un gran nombre d’interaccions per augmentar el nostre coneixement dels microbiomes complexos. Això és un repte a causa del gran nombre d'interaccions possibles. Per això, les interaccions microbianes encara són poc conegudes fins ara. Les xarxes són una gran eina per tractar el gran nombre de microorganismes i les seves connexions, explorar interaccions microbianes potencials i dilucidar patrons d’ecosistemes microbians. Aquesta tesi es situa a la intersecció de la inferència de xarxes i l’anàlisi de la xarxes. La metodologia presentada té com a objectiu donar suport i avançar en investigacions microbianes marines reduint el soroll i dilucidant patrons en xarxes d’associació inferides per a posteriors anàlisis biològiques. La principal contribució d’aquesta tesi als estudis d’interaccions microbianes marines és el desenvolupament del programa EnDED (Environmentally-Driven Edge Detection), un marc computacional per identificar associacions impulsades pel medi ambient dins de xarxes d’associació microbiana, inferides a partir de conjunts de dades òmics. S’ha aplicat la metodologia a un model d’ecosistema microbià marí a l’Observatori Microbià de la Badia de Blanes (BBMO) al mar Mediterrani nord-occidental (deu anys de mostreig mensual). També s’ha la metodologia a una recopilació de dades que cobreix sis regions oceàniques globals des de la superfície (3 m) fins a l'oceà profund (fins a 4539 m).Hay una gran cantidad de microorganismos en la Tierra que contribuyen a los ciclos biogeoquímicos globales, y sus interacciones se consideran fundamentales para la función del ecosistema. Estudios previos ya han determinado relaciones entre un número limitado de microorganismos. Sin embargo, todavía necesitamos comprender una gran cantidad de interacciones para aumentar nuestro conocimiento de los microbiomas más complejos. Esto representa un gran desafío debido a la gran cantidad de posibles interacciones. Por lo tanto, las interacciones microbianas son aun poco conocidas. Las redes representan una gran herramienta para analizar la gran cantidad de microorganismos y sus conexiones, explorar posibles interacciones y dilucidar patrones en ecosistemas microbianos. Esta tesis se ubica en la intersección entre la inferencia de redes y el análisis de redes. La metodología presentada tiene como objetivo avanzar las investigaciones sobre interacciones microbianas marinas mediante la reducción del ruido en las inferencias de redes y elucidar patrones en redes de asociación permitiendo análisis biológicos posteriores. La principal contribución de esta tesis a los estudios de interacciones microbianas marinas es el desarrollo del programa EnDED (Environmentally-Driven Edge Detection), un marco computacional para identificar asociaciones generadas por el medio ambiente en redes de asociaciones microbianas, inferidas a partir de datos ómicos. Aplicamos la metodología a un modelo de ecosistema microbiano marino en el Observatorio Microbiano de la Bahía de Blanes (BBMO) en el Mar Mediterráneo Noroccidental (diez años de muestreo mensual). También, aplicamos la metodología a una compilación de conjuntos de datos que cubren seis regiones oceánicas globales desde la superficie (3 m) hasta las profundidades del océano (hasta 4539 m). Por lo tanto, nuestra metodología significa un paso adelante hacia de los patrones temporales microbianos marinos y el estudio de la distribución microbiana marina en el espacio a través de los ejes horizontal (regiones oceánicas) y vertical (columna de agua). Para llegar a hipótesis de interacción precisas, es importante determinar, cuantificar y eliminar las asociaciones generadas por el medio ambiente en las redes de asociaciones microbianas marinas. Además, nuestros resultados subrayaron la necesidad de estudiar la naturaleza dinámica de las redes, en contraste con el uso de redes estáticas únicas agregadas en el tiempo o el espacio. Nuestras nuevas metodologías pueden ser utilizadas por una amplia gama de investigadores que investigan redes e interacciones en diversos microbiomas.Postprint (published version

Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases.

Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non-demented controls, we investigated global disruptions in the co-regulation of genes in two neurodegenerative diseases, late-onset Alzheimer's disease (AD) and Huntington's disease (HD). We identified networks of differentially co-expressed (DC) gene pairs that either gained or lost correlation in disease cases relative to the control group, with the former dominant for both AD and HD and both patterns replicating in independent human cohorts of AD and aging. When aligning networks of DC patterns and physical interactions, we identified a 242-gene subnetwork enriched for independent AD/HD signatures. This subnetwork revealed a surprising dichotomy of gained/lost correlations among two inter-connected processes, chromatin organization and neural differentiation, and included DNA methyltransferases, DNMT1 and DNMT3A, of which we predicted the former but not latter as a key regulator. To validate the inter-connection of these two processes and our key regulator prediction, we generated two brain-specific knockout (KO) mice and show that Dnmt1 KO signature significantly overlaps with the subnetwork (P = 3.1 × 10(-12)), while Dnmt3a KO signature does not (P = 0.017)

Kinetic modelling of competition and depletion of shared miRNAs by competing endogenous RNAs

Non-conding RNAs play a key role in the post-transcriptional regulation of mRNA translation and turnover in eukaryotes. miRNAs, in particular, interact with their target RNAs through protein-mediated, sequence-specific binding, giving rise to extended and highly heterogeneous miRNA-RNA interaction networks. Within such networks, competition to bind miRNAs can generate an effective positive coupling between their targets. Competing endogenous RNAs (ceRNAs) can in turn regulate each other through miRNA-mediated crosstalk. Albeit potentially weak, ceRNA interactions can occur both dynamically, affecting e.g. the regulatory clock, and at stationarity, in which case ceRNA networks as a whole can be implicated in the composition of the cell's proteome. Many features of ceRNA interactions, including the conditions under which they become significant, can be unraveled by mathematical and in silico models. We review the understanding of the ceRNA effect obtained within such frameworks, focusing on the methods employed to quantify it, its role in the processing of gene expression noise, and how network topology can determine its reach.Comment: review article, 29 pages, 7 figure

Disentangling the multigenic and pleiotropic nature of molecular function

Background: Biological processes at the molecular level are usually represented by molecular interaction networks. Function is organised and modularity identified based on network topology, however, this approach often fails to account for the dynamic and multifunctional nature of molecular components. For example, a molecule engaging in spatially or temporally independent functions may be inappropriately clustered into a single functional module. To capture biologically meaningful sets of interacting molecules, we use experimentally defined pathways as spatial/temporal units of molecular activity. Results: We defined functional profiles of Saccharomyces cerevisiae based on a minimal set of Gene Ontology terms sufficient to represent each pathway's genes. The Gene Ontology terms were used to annotate 271 pathways, accounting for pathway multi-functionality and gene pleiotropy. Pathways were then arranged into a network, linked by shared functionality. Of the genes in our data set, 44% appeared in multiple pathways performing a diverse set of functions. Linking pathways by overlapping functionality revealed a modular network with energy metabolism forming a sparse centre, surrounded by several denser clusters comprised of regulatory and metabolic pathways. Signalling pathways formed a relatively discrete cluster connected to the centre of the network. Genetic interactions were enriched within the clusters of pathways by a factor of 5.5, confirming the organisation of our pathway network is biologically significant. Conclusions: Our representation of molecular function according to pathway relationships enables analysis of gene/protein activity in the context of specific functional roles, as an alternative to typical molecule-centric graph-based methods. The pathway network demonstrates the cooperation of multiple pathways to perform biological processes and organises pathways into functionally related clusters with interdependent outcomes

Mini-Workshop: Recent Developments in Statistical Methods with Applications to Genetics and Genomics

Recent progress in high-throughput genomic technologies has revolutionized the field of human genetics and promises to lead to important scientific advances. With new improvements in massively parallel biotechnologies, it is becoming increasingly more efficient to generate vast amounts of information at the genomics, transcriptomics, proteomics, metabolomics etc. levels, opening up as yet unexplored opportunities in the search for the genetic causes of complex traits. Despite this tremendous progress in data generation, it remains very challenging to analyze, integrate and interpret these data. The resulting data are high-dimensional and very sparse, and efficient statistical methods are critical in order to extract the rich information contained in these data. The major focus of the mini-workshop, entitled “Recent Developments in Statistical Methods with Applications to Genetics and Genomics”, has been on integrative methods. Relevant research questions included the optimal study design for integrative genomic analyses; appropriate handling and pre-processing of different types of omics data; statistical methods for integration of multiple types of omics data; adjustment for confounding due to latent factors such as cell or tissue heterogeneity; the optimal use of omics data to enhance or make sense of results identified through genetic studies; and statistical and computational strategies for analysis of multiple types of high-dimensional data