Randomization techniques for assessing the significance of gene periodicity results

Peer reviewe

Characterizing regulatory path motifs in integrated networks using perturbational data

Pathicular – a Cytoscape plugin for analysing cellular responses to transcription factor perturbations is presente

Discovery of relevant response in infected potato plants from time series of gene expression data

The paper presents a methodology for analyzing time series of gene expression data collected from the leaves of potato virus Y (PVY) infected and non-infected potato plants, with the aim to identify significant differences between the two sets of potato plants’ characteristic for various time points. We aim at identifying differentially- expressed genes whose expression values are statistically significantly different in the set of PVY infected potato plants compared to non- infected plants, and which demonstrate also statistically significant changes of expression values of genes of PVY infected potato plants in time. The novelty of the approach includes stratified data randomization used in estimating the statistical properties of gene expression of the samples in the control set of non-infected potato plants. A novel estimate that computes the relative minimal distance between the samples has been defined that enables reliable identification of the differences between the target and control datasets when these sets are small. The relevance of the outcomes is demonstrated by visualizing the relative minimal distance of gene expression changes in time for three different types of potato leaves for the genes that have been identified as relevant by the proposed methodology

Peridocity, Change Detection and Prediction in Microarrays

Three topics in the analysis of microarray genomic data are discussed and improved statistical methods are developed in each case. A statistical test with higher power is developed for detecting periodicity in microarray time series data. Periodicity in short series, with non-Fourier frequencies, is detected through a Pearson curve calibrated to the null distribution obtained by computer simulation. Unlike other traditional methods, this approach is applicable even in the presence of missing values or unequal time intervals. The usefulness of the new method is demonstrated on simulated series as well as actual microarray time series. The second topic develops a new method for detection of changes in DNA or gene copy number. Regions for DNA copy number aberrations in chromosomal material are detected using maximum overlapping discrete wavelet transform (MODWT). It is shown how repeated application of MODWT to a series can be used to confirm the presence of change points. Application to simulated as well as array CGH (Comparative Genomic Hybridization) data confirms the excellent performance of this method. In the third topic, it is shown that an improved class predictor for tissue samples in microarray experiments is developed by incorporating nearest neighbour covariates (NNC). It is demonstrated that this method reduces the mis-classification errors in both simulated and actual microarray data

New methods for the study of Primary Ciliary Dyskinesia

Os cilios e flagelos são projeções celulares encontradas nas células eucariotas, são altamente conservados entre espécies e envolvidos na locomoção e movimentação de fluídos. A Discinésia Ciliar Primária (DCP) é uma doenca genética autossómica recessiva dos cílios móveis, que tem como consequência várias manifestações clínicas. Estima-se que a DCP afete ~1 em cada 10.000 pessoas, mas é mais prevalente em grupos com marcada consanguinidade. A DCP está associada até à data a mais de 40 genes causadores de doença. O diagnóstico da DCP envolve a combinação de vários testes, entre eles a microscopia electrónica (ME), teste determinante na classificação de anomalias ciliares. Neste trabalho foquei-me nos cílios móveis e em como se classificam as derivações à estrutura considerada normal. Este estudo levou ao desenvolvimento de feramentas e diretrizes que tornam o diagnóstico de DCP por EM mais estandardizado, informativo e fidedigno. A DCP necessita de ser modelada em organismos vertebrados como o ratinho, a rã e o peixe-zebra (PZ) para melhor conhecimento dos seus mecanismos moleculares. O PZ é um bom modelo de DCP porque apresenta diversos órgãos ciliados durante os estados larvares (cílios moveis e imoveis) e tem, até agora, homólogos de todos os genes causadores da doença humana. Desta forma a utilização de peixes mutantes tem sido um bom contributo para compreender esta doença humana. Neste trabalho investiguei por ME dois tipos de cílios móveis do PZ concluindo que estes apresentam semelhanças estruturais conservadas com os cílios móveis das vias aéreas do ser humano saudável e com DCP.Cilia and flagella are cellular protrusions found in eucaryotic cells, highly conserved between species and found in almost every cell type. Motile cilia are known for their motility properties and are involved in propelling and moving fluids. Primary ciliary dyskinesia (PCD) is an inherited autosomal-recessive disorder of motile cilia that results in several clinical manifestations. The estimated prevalence of PCD is ∼1 per 10,000 births, but it is more prevalent in populations where consanguinity is common, it is currently associated with mutations in more than 40 genes. To diagnose PCD it involves a combination of tests, in particular, electron microscopy (EM) that is essential for determining the type of ciliary ultrastructural defect. In this work I have focused on motile cilia ultrastructure and how the differences in cilia can be identified and classified, through the development of tools and guidelines to make the quantification and analysis of cilia more reliable and informative. The differential diagnosis of PCD is complex but crucial, and the development of new potential targeted treatments is essential. For better investigating the molecular mechanisms underlying PCD, it has been modelled in several organisms like mice, frogs and Zebrafish (ZF). ZF is a teleost vertebrate used in many areas of research, and a well-known animal model. ZF embryos develop quickly and allow unique advantages for research studies owing to their transparency during larval stages. ZF has many ciliated organs and presents primary cilia as well as motile cilia together with homologs for all the disease causing genes. The use of mutant zebrafish has been contributing to the better understanding of PCD molecular aetiology. Here, I investigated whether zebrafish cilia are ultrastructurally suitable for the study of PCD and concluded that the motile cilia of zebrafish resemble the cilia in the human airway in healthy conditions and in PCD

Characterization of Type I Collagen and Osteoblast Response to Mechanical Loading

Bone is a composite material made up of an inorganic (hydroxyapatite mineral) phase, a proteinaceous organic phase, and water. Comprising 90% of bones organic phase, type I collagen is the most abundant protein in the human body. Both hydroxyapatite and collagen contribute to bone mechanical properties, and because bone is a hierarchical material, changes in properties of either phase can influence bulk mechanical properties of the tissue and bone structure. Type I collagen in bone is synthesized by osteoblasts as a helical structure formed from three polypeptide chains of amino acids. These molecules are staggered into an array and the resulting collagen fibrils are stabilized by crosslinks. Enzymatic crosslinking can be limited by compounds such as -aminopropionitrile (BAPN) and result in a crosslink deficiency characterizing a disease known as lathyrism. BAPN acts by irreversibly binding to the active site of the lysyl oxidase enzyme, blocking the formation of new crosslinks and the maturation of pre-existing immature crosslinks. Understanding how changes in bone properties on a cellular level transcend levels of bone hierarchy provides an opportunity to detect or diagnose bone disease before disease-related changes are expressed at the organ or tissue level. This dissertation studies the in vitro effect of BAPN-induced enzymatic crosslink reduction on osteoblast-produced collagen nanostructure, mechanical properties, crosslink ratio, and expression of genes related to type I collagen synthesis and crosslinking. The work also explores the effect of mechanical loading via applied substrate strain on these properties to investigate its potential compensatory impact

Advancing Precision Medicine: Unveiling Disease Trajectories, Decoding Biomarkers, and Tailoring Individual Treatments

Chronic diseases are not only prevalent but also exert a considerable strain on the healthcare system, individuals, and communities. Nearly half of all Americans suffer from at least one chronic disease, which is still growing. The development of machine learning has brought new directions to chronic disease analysis. Many data scientists have devoted themselves to understanding how a disease progresses over time, which can lead to better patient management, identification of disease stages, and targeted interventions. However, due to the slow progression of chronic disease, symptoms are barely noticed until the disease is advanced, challenging early detection. Meanwhile, chronic diseases often have diverse underlying causes and can manifest differently among patients. Besides the external factors, the development of chronic disease is also influenced by internal signals. The DNA sequence-level differences have been proven responsible for constant predisposition to chronic diseases. Given these challenges, data must be analyzed at various scales, ranging from single nucleotide polymorphisms (SNPs) to individuals and populations, to better understand disease mechanisms and provide precision medicine. Therefore, this research aimed to develop an automated pipeline from building predictive models and estimating individual treatment effects based on the structured data of general electronic health records (EHRs) to identifying genetic variations (e.g., SNPs) associated with diseases to unravel the genetic underpinnings of chronic diseases. First, we used structured EHRs to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. In this step, we employed causal inference methods (constraint-based and functional causal models) for feature selection and utilized Markov chains, attention long short-term memory (LSTM), and Gaussian process (GP). SHapley Additive exPlanations (SHAPs) and local interpretable model-agnostic explanations (LIMEs) further extended the work to identify important clinical features. Next, I developed a novel counterfactual-based method to predict individual treatment effects (ITE) from observational data. To discern a “balanced” representation so that treated and control distributions look similar, we disentangled the doctor’s preference from the covariance and rebuilt the representation of the treated and control groups. We use integral probability metrics to measure distances between distributions. The expected ITE estimation error of a representation was the sum of the standard generalization error of that representation and the distance between the distributions induced. Finally, we performed genome-wide association studies (GWAS) based on the stage information we extracted from our unsupervised disease progression model to identify the biomarkers and explore the genetic correction between the disease and its phenotypes

Temporal Networks

A great variety of systems in nature, society and technology -- from the web of sexual contacts to the Internet, from the nervous system to power grids -- can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks