Digital twins to personalize medicine

Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient

Identification of genes and regulators that are shared across T cell associated diseases

Genome-wide association studies (GWASs) of hundreds of diseases and millions of patients have led to the identification of genes that are associated with more than one disease. The aims of this PhD thesis were to a) identify a group of genes important in multiple diseases (shared disease genes), b) identify shared up-stream disease regulators, and c) determine how the same genes can be involved in the pathogenesis of different diseases. These aims have been tested on CD4+ T cells because they express the T helper cell differentiation pathway, which was the most enriched pathway in analyses of all disease associated genes identified with GWASs. Combining information about known gene-gene interactions from the protein-protein interaction (PPI) network with gene expression changes in multiple T cell associated diseases led to the identification of a group of highly interconnected genes that were miss-expressed in many of those diseases – hereafter called ‘shared disease genes’. Those genes were further enriched for inflammatory, metabolic and proliferative pathways, genetic variants identified by all GWASs, as well as mutations in cancer studies and known diagnostic and therapeutic targets. Taken together, these findings supported the relevance of the shared disease genes. Identification of the shared upstream disease regulators was addressed in the second project of this PhD thesis. The underlying hypothesis assumed that the determination of the shared upstream disease regulators is possible through a network model showing in which order genes activate each other. For that reason a transcription factor–gene regulatory network (TF-GRN) was created. The TF-GRN was based on the time-series gene expression profiling of the T helper cell type 1 (Th1), and T helper cell type 2 (Th2) differentiation from Native T-cells. Transcription factors (TFs) whose expression changed early during polarization and had many downstream predicted targets (hubs) that were enriched for disease associated single nucleotide polymorphisms (SNPs) were prioritised as the putative early disease regulators. These analyses identified three transcription factors: GATA3, MAF and MYB. Their predicted targets were validated by ChIP-Seq and siRNA mediated knockdown in primary human T-cells. CD4+ T cells isolated from seasonal allergic rhinitis (SAR) and multiple sclerosis (MS) patients in their non-symptomatic stages were analysed in order to demonstrate predictive potential of those three TFs. We found that those three TFs were differentially expressed in symptom-free stages of the two diseases, while their TF-GRN{predicted targets were differentially expressed during symptomatic disease stages. Moreover, using RNA-Seq data we identified a disease associated SNP that correlated with differential splicing of GATA3. A limitation of the above study is that it concentrated on TFs as main regulators in cells, excluding other potential regulators such as microRNAs. To this end, a microRNA{gene regulatory network (mGRN) of human CD4+ T cell differentiation was constructed. Within this network, we defined regulatory clusters (groups of microRNAs that are regulating groups of mRNAs). One regulatory cluster was differentially expressed in all of the tested diseases, and was highly enriched for GWAS SNPs. Although the microRNA processing machinery was dynamically upregulated during early T-cell activation, the majority of microRNA modules showed specialisation in later time-points. In summary this PhD thesis shows the relevance of shared genes and up-stream disease regulators. Putative mechanisms of why shared genes can be involved in pathogenesis of different diseases have also been demonstrated: a) differential gene expression in different diseases; b) alternative transcription factor splicing variants may affect different downstream gene target group; and c) SNPs might cause alternative splicing

Особенности эрозивных поражений суставов у пациентов с разной длительностью ревматоидгного артрита

артрит ревматоидныйсуставов болезн

The Allergic Airway Inflammation Repository - a user-friendly, curated resource of mRNA expression levels in studies of allergic airways

Public microarray databases allow analysis of expression levels of candidate genes in different contexts. However, finding relevant microarray data is complicated by the large number of available studies. We have compiled a user-friendly, open-access database of mRNA microarray experiments relevant to allergic airway inflammation, the Allergic Airway Inflammation Repository (AAIR, http://aair.cimed.ike.liu.se/). The aim is to allow allergy researchers to determine the expression profile of their genes of interest in multiple clinical data sets and several experimental systems quickly and intuitively. AAIR also provides quick links to other relevant information such as experimental protocols, related literature and raw data files

Dynamic Response Genes in CD4+ T Cells Reveal a Network of Interactive Proteins that Classifies Disease Activity in Multiple Sclerosis

Multiple sclerosis (MS) is a chronic inflammatory disease of the CNS and has a varying disease course as well as variable response to treatment. Biomarkers may therefore aid personalized treatment. We tested whether in vitro activation of MS patient-derived CD4+ T cells could reveal potential biomarkers. The dynamic gene expression response to activation was dysregulated in patient-derived CD4+ T cells. By integrating our findings with genome-wide association studies, we constructed a highly connected MS gene module, disclosing cell activation and chemotaxis as central components. Changes in several module genes were associated with differences in protein levels, which were measurable in cerebrospinal fluid and were used to classify patients from control individuals. In addition, these measurements could predict disease activity after 2 years and distinguish low and high responders to treatment in two additional, independent cohorts. While further validation is needed in larger cohorts prior to clinical implementation, we have uncovered a set of potentially promising biomarkers

LASSIM inferred a robust minimal and full-scale non-linear transcription factor—Target dynamic system describing naïve T-cells towards Th2 cells.

<p>(A) We identified 12 core Th2 driving TFs from the literature, and inferred their putative targets using DNase-seq data from ENCODE. In total, these interactions constituted of 63 core TF-TF interactions and 64,872 core-to-peripheral gene regulations. These interactions were assumed to follow a sigmoid function, as described in the Methods section. The complete prior network was, together with Th2 differentiation dynamics and siRNA mediated knock down data of each TF measured by microarray profiling, used by LASSIM to infer a Th2 core system. As can be seen in the core network, there are feedback loops between several of the TFs. (B) Microarray time series experiments (red dots) and respective state simulated by the LASSIM model (blue solid lines) of the core TFs. On the x-axis is time, and the y-axis denotes gene expression in arbitrary units. (C) Heat map of the data fit of the Th2 model to the siRNA perturbation data, i.e. the siRNA part of <i>V</i>(<b><i>p</i></b><i>*</i>). Each siRNA knock-down experiment is represented as a separate column. For example, the model fits the response of a siRNA knock-down on IRF4 well for all TFs except MAF well. (D) Box-plot representing the ranking of each removed parameter from multiple stochastic optimizations of the core model. All edges that had a median selection rank over 40 were included in the final model. Model selection was based on prediction error variation, see section <i>model selection</i> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005608#pcbi.1005608.s003" target="_blank">S3 Fig</a>.</p

The edges from the naïve Th2 model were better refitted to total Th2 differentiation than the prior network.

<p>(A) We refitted the core network to new time-series data from total Th2-cells by keeping the signs of the inferred minimal Th2 model. (B) The fit of the core model to the total T-cell data is shown. The y-axis has arbitrary units of expression, and the x-axis is time. The model output is denoted by the blue curves, and the data are shown by the red points. (C) The fit of the peripheral genes. The figure follows the same style as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005608#pcbi.1005608.g003" target="_blank">Fig 3B</a>. The black band in the cost bar denotes the 0.95 rejection limit of the corresponding χ²-test, with all rejected models above the band. (D) We resampled our prior matrix with the same number of parameters and signs as the Th2 model 10,000 times, and compared the random models with the output from LASSIM. The ability of the inferred core network structure to fit novel gene expression data of total T-activation was considered. A total of 1 000 random core model structures were drawn from the prior network, and refitted to the data. The distribution of the fit is shown by the blue curve. The core network identified from LASSIM was shown to be significantly better for fitting the novel data, as marked by the black arrow. Moreover, most of the core models could be rejected by a χ²-test, and are represented in red.</p

General workflow of the LArge-Scale SIMulation (LASSIM) high performance toolbox.

<p>As <b>Input</b> LASSIM take four basic components, <i>core and peripheral prior networks</i> (optional), <i>experimental data</i> and <i>dynamic equations</i> to fit a large-scale non-linear dynamic system based on the fully parallel PyGMO toolbox. <b>Step 1</b>, LASSIM performs pruning and data fitting on the core system. <b>Step 2</b>, LASSIM expands the core model by inferring outgoing interactions with peripheral genes, with each gene solved in parallel using a computer cluster. The LASSIM functions are fully modular, and have been built so that the functions describing the optimization procedure, dynamic equations, cost function and data pruning are modular and can easily be changed by the user. The <b>Output</b> is either the core network defined in <b>Step 1</b> or a genome wide regulatory network if <b>Step 2</b> is run, with ranked interactions based on selection order, as well as kinetic parameters for the dynamic equations.</p

LASSIM inferred a genome-wide model of 35,900 core-to-peripheral gene interactions.

<p>(A) These peripheral genes do not have any feedbacks to the core system, nor any crosstalk among themselves. (B) The measured mRNA profiles of the 10,543 peripheral genes (blue/yellow represent relative low/high expression), sorted by the model cost (<i>V</i>(<b><i>p</i></b><i>*</i>)) on the y-axis, and time points on the x-axis. As can be seen, genes that display a peak in expression at the second or third time points are generally associated with a higher cost. (C) The results from the ChIP-seq analysis of inferred to-gene interactions, where the y-axis shows the—log₁₀(p). The red line denotes the significance level (Bootstrap P < 0.05).</p