Cell cycle networks link gene expression dysregulation, mutation, and brain maldevelopment in autistic toddlers

Genetic mechanisms underlying abnormal early neural development in toddlers with Autism Spectrum Disorder (ASD) remain uncertain due to the impossibility of direct brain gene expression measurement during critical periods of early development. Recent findings from a multi‐tissue study demonstrated high expression of many of the same gene networks between blood and brain tissues, in particular with cell cycle functions. We explored relationships between blood gene expression and total brain volume (TBV) in 142 ASD and control male toddlers. In control toddlers, TBV variation significantly correlated with cell cycle and protein folding gene networks, potentially impacting neuron number and synapse development. In ASD toddlers, their correlations with brain size were lost as a result of considerable changes in network organization, while cell adhesion gene networks significantly correlated with TBV variation. Cell cycle networks detected in blood are highly preserved in the human brain and are upregulated during prenatal states of development. Overall, alterations were more pronounced in bigger brains. We identified 23 candidate genes for brain maldevelopment linked to 32 genes frequently mutated in ASD. The integrated network includes genes that are dysregulated in leukocyte and/or postmortem brain tissue of ASD subjects and belong to signaling pathways regulating cell cycle G1/S and G2/M phase transition. Finally, analyses of the CHD8 subnetwork and altered transcript levels from an independent study of CHD8 suppression further confirmed the central role of genes regulating neurogenesis and cell adhesion processes in ASD brain maldevelopment

Role of network topology based methods in discovering novel gene-phenotype associations

The cell is governed by the complex interactions among various types of biomolecules. Coupled with environmental factors, variations in DNA can cause alterations in normal gene function and lead to a disease condition. Often, such disease phenotypes involve coordinated dysregulation of multiple genes that implicate inter-connected pathways. Towards a better understanding and characterization of mechanisms underlying human diseases, here, I present GUILD, a network-based disease-gene prioritization framework. GUILD associates genes with diseases using the global topology of the protein-protein interaction network and an initial set of genes known to be implicated in the disease. Furthermore, I investigate the mechanistic relationships between disease-genes and explain the robustness emerging from these relationships. I also introduce GUILDify, an online and user-friendly tool which prioritizes genes for their association to any user-provided phenotype. Finally, I describe current state-of-the-art systems-biology approaches where network modeling has helped extending our view on diseases such as cancer.La cèl•lula es regeix per interaccions complexes entre diferents tipus de biomolècules. Juntament amb factors ambientals, variacions en el DNA poden causar alteracions en la funció normal dels gens i provocar malalties. Sovint, aquests fenotips de malaltia involucren una desregulació coordinada de múltiples gens implicats en vies interconnectades. Per tal de comprendre i caracteritzar millor els mecanismes subjacents en malalties humanes, en aquesta tesis presento el programa GUILD, una plataforma que prioritza gens relacionats amb una malaltia en concret fent us de la topologia de xarxe. A partir d’un conjunt conegut de gens implicats en una malaltia, GUILD associa altres gens amb la malaltia mitjancant la topologia global de la xarxa d’interaccions de proteïnes. A més a més, analitzo les relacions mecanístiques entre gens associats a malalties i explico la robustesa es desprèn d’aquesta anàlisi. També presento GUILDify, un servidor web de fácil ús per la priorització de gens i la seva associació a un determinat fenotip. Finalment, descric els mètodes més recents en què el model•latge de xarxes ha ajudat extendre el coneixement sobre malalties complexes, com per exemple a càncer

Neural Alterations Influencing Skilled Reading In Adhd: A Task-Based Fmri Study

Attention-Deficit/Hyperactivity Disorder (ADHD) is a heterogeneous, neurodevelopmental disorder which co-occurs often with Reading Disability (RD). ADHD with and without RD consistently have higher inattentive ratings compared with typically developing controls, with co-occurring ADHD and RD (ADHD/+RD) also demonstrating impaired phonological processing. Accordingly, inattention has been associated with greater phonological impairment, though neither the neural correlates of the co-occurring disorders nor the association are well understood from a functional neuroimaging perspective. The goal was to assess to what extent ADHD/+RD differ from ADHD without RD (ADHD/-RD) and typically developing controls (TDC) in functional activation of attention- and reading-related areas during various tasks. The general hypothesis was that ADHD/+RD would show more extensive alterations in attention-related areas and unique alterations in reading-related areas compared with the other two groups. The results indicated differences between ADHD/+RD and ADHD/-RD in attention processing; ADHD/-RD showed greater activation of frontoparietal areas for digit and word rhyming continuous performance fMRI tasks. Additionally, though some dysfunction was observed in decoding-related areas in ADHD/+RD relative to TDC, the results showed greater evidence of other cognitive impairments influencing decoding abilities across the ADHD/+RD and ADHD/-RD. Once the groups were re-characterized to reflect relative reading abilities in decoding and word recognition, specific cognitive and functional activation profiles surfaced for three groups: Balanced, Decoders, and Sight Readers. Two findings contribute to a better understanding of ADHD and RD. First, the functional activation differences between the ADHD subgroups suggest that RD needs to be characterized specifically in ADHD neuroimaging studies and that non-linguistic stimuli should be used to mitigate RD-related confounds in ADHD studies. Second, the role of cognitive impairments, including the level of inattention, on phonology requires clarification from a neuroimaging perspective. Lastly, the novel Reading Tendency Index provides an estimation of an individual\u27s preferred strategy for single word reading without the influence of variable processing speeds. The Index corresponds with predictable neural activations and has implications for instructional and remediation practices

Interactive effects of dopamine transporter genotype and aging on resting-state functional networks

Aging and dopamine modulation have both been independently shown to influence the functional connectivity of brain networks during rest. Dopamine modulation is known to decline during the course of aging. Previous evidence also shows that the dopamine transporter gene (DAT1) influences the re-uptake of dopamine and the anyA9 genotype of this gene is associated with higher striatal dopamine signaling. Expanding these two lines of prior research, we investigated potential interactive effects between aging and individual variations in the DAT1 gene on the modular organization of brain acvitiy during rest. The graph-theoretic metrics of modularity, betweenness centrality and participation coefficient were assessed in 41 younger (age 20-30 years) and 37 older (age 60-75 years) adults. Age differences were only observed in the participation coefficient in carriers of the anyA9 genotype of the DAT1 gene and this effect was most prominently observed in the default mode network. Furthermore, we found that individual differences in the values of the participation coefficient correlated with individual differences in fluid intelligence and a measure of executive control in the anyA9 carriers. The correlation between participation coefficient and fluid intelligence was mainly shared with age-related differences, whereas the correlation with executive control was independent of age. These findings suggest that DAT1 genotype moderates age differences in the functional integration of brain networks as well as the relation between network characteristics and cognitive abilities

A Knowledge-based Integrative Modeling Approach for <em>In-Silico</em> Identification of Mechanistic Targets in Neurodegeneration with Focus on Alzheimer’s Disease

Dementia is the progressive decline in cognitive function due to damage or disease in the body beyond what might be expected from normal aging. Based on neuropathological and clinical criteria, dementia includes a spectrum of diseases, namely Alzheimer's dementia, Parkinson's dementia, Lewy Body disease, Alzheimer's dementia with Parkinson's, Pick's disease, Semantic dementia, and large and small vessel disease. It is thought that these disorders result from a combination of genetic and environmental risk factors. Despite accumulating knowledge that has been gained about pathophysiological and clinical characteristics of the disease, no coherent and integrative picture of molecular mechanisms underlying neurodegeneration in Alzheimer’s disease is available. Existing drugs only offer symptomatic relief to the patients and lack any efficient disease-modifying effects. The present research proposes a knowledge-based rationale towards integrative modeling of disease mechanism for identifying potential candidate targets and biomarkers in Alzheimer’s disease. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. It prepares the ground for transitioning from ‘descriptive’ to “mechanistic” representation of disease processes. The proposed approach was used to introduce an integrative framework, which integrates, on one hand, extracted knowledge from the literature using semantically supported text-mining technologies and, on the other hand, primary experimental data such as gene/protein expression or imaging readouts. The aim of such a hybrid integrative modeling approach was not only to provide a consolidated systems view on the disease mechanism as a whole but also to increase specificity and sensitivity of the mechanistic model by providing disease-specific context. This approach was successfully used for correlating clinical manifestations of the disease to their corresponding molecular events and led to the identification and modeling of three important mechanistic components underlying Alzheimer’s dementia, namely the CNS, the immune system and the endocrine components. These models were validated using a novel in-silico validation method, namely biomarker-guided pathway analysis and a pathway-based target identification approach was introduced, which resulted in the identification of the MAPK signaling pathway as a potential candidate target at the crossroad of the triad components underlying disease mechanism in Alzheimer’s dementia

Detection of Epigenomic Network Community Oncomarkers

In this paper we propose network methodology to infer prognostic cancer biomarkers based on the epigenetic pattern DNA methylation. Epigenetic processes such as DNA methylation reflect environmental risk factors, and are increasingly recognised for their fundamental role in diseases such as cancer. DNA methylation is a gene-regulatory pattern, and hence provides a means by which to assess genomic regulatory interactions. Network models are a natural way to represent and analyse groups of such interactions. The utility of network models also increases as the quantity of data and number of variables increase, making them increasingly relevant to large-scale genomic studies. We propose methodology to infer prognostic genomic networks from a DNA methylation-based measure of genomic interaction and association. We then show how to identify prognostic biomarkers from such networks, which we term `network community oncomarkers'. We illustrate the power of our proposed methodology in the context of a large publicly available breast cancer dataset

Automatic classification of medical images based on functional connectivity measurements - methodological exploration

The study of patterns of neuronal activity constitutes a tool of extreme value in the attempt to unveil neural pathological mechanisms. Hence, functional connectivity studies using images from Resting State fMRI (rs-fMRI) are crucial, and there are several metrics which can be used to assess brain connections. Nonetheless, no clear evidence exists that some may be better than others. In this study, in an attempt to discover if certain metrics better characterized certain connections, two different approaches were followed. Data from a public dataset was used - Addiction Connectome Preprocessed Initiative (ACPI) - as well as one toolbox for matrix construction - Multiple Connectivity Analysis (MULAN) - and another for statistical comparison - GraphVar. Both toolboxes run in MATLAB. Metrics under analysis were: correlation, coherence, mutual information, transfer entropy and non-linear correlation. To that end, 116 brain regions were considered. First, considering only healthy subjects, it was done a pairwise comparison between results from different metrics. It was verified that each of them led to different results regarding the same connections. Then, connectivity results between a healthy and a pathological group of subjects with Attention-Deficit/Hyperactivity Disorder (ADHD) were compared. Concerning the differences, several similarities with the known affected areas described amongst the literature were found. However, discrepancies were observed which may be related to differences in sample size and/or the metric used in these studies. In general, it was shown that there is indeed variability between functional metrics and regional specificity. Still, the anatomical and physiological reasons for these differences remain unknown. It was clear that using more than one metric may be important and that the use of more general metrics may have advantages in the study of the pathological brain as it may have more complex dynamics. Furthermore, ensemble tools that have into consideration more than one metric to characterize brain connections may represent invaluable tools for autonomic image classification