Defence of doctoral thesis in the field of Computer Science, M.Sc. (Tech) Juhi Somani

Title of the thesis is: "Statistical and computational analysis of high-throughput ‘omics’ datasets for understanding the etiology and pathogenesis of autoimmune diseases"
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The primary function of the human immune system is to protect us from harmful substances and microbes (i.e. pathogens) that we continuously encounter through our surroundings. However, a variety of factors can lead to immune system dysfunction, which in turn can give rise to various incurable diseases, including autoimmune diseases (ADs), such as type 1 diabetes (T1D), immunoglobulin G4 related disease (IgG4-RD) and systemic sclerosis (SSc). In ADs, the immune system fails to distinguish between pathogens and body’s own cells, and erroneously attacks the healthy tissues. Unfortunately, the factors that trigger ADs (i.e. etiology) and the molecular mechanisms by which ADs develop (i.e. pathogenesis) remain poorly understood. Genetics and environmental factors, such as gut microbiome, have been implicated in influencing the development of ADs, but the concerned mechanisms remain largely elusive. Therefore, this thesis aims to further our understanding about the etiology and pathogenesis of ADs by performing robust statistical and computational analyses on high-throughput ‘omics’ datasets.

We studied transcriptomics data from immune cells of T1D susceptible infants to identify gene expression markers that can aid in predicting the onset of autoimmunity and characterizing the disease progression. We found several genes to be associated with the pathogenesis of T1D, including cytokine IL32 that has not been associated with T1D before. 

We also developed a personalised method for robustly modelling longitudinal transcriptomics data from heterogeneous diseases and identify pathways associated with the pathogenesis of the disease. This method was able to associate several key pathways to T1D pathogenesis that were missed by other methods. 

Additionally, we studied the gut microbiome of IgG4-RD and SSc patients and identified potential sources of microbial signals that may be contributing to the etiology of the two diseases, including a significant overabundance of a specific strain of Eggerthella lenta that contains genes with the potential of influencing the immune system. 

Finally, we aimed to identify environmental and host-related factors that may be influencing the development of the early gut microbiome of T1D susceptible infants. In effect, we linked several new factors to the development of the early gut microbiome, such as household location at birth, maternal antibiotic treatments and average increase in height and weight per year, to name a few. 

Opponent: professor Erik Bongcam-Rudloff, Swedish University of Agricultural Sciences, Sweden

Custos: professor Harri Lähdesmäki, Aalto University School of Science, Department of Computer Science

Contact information of the doctoral student: [email protected]

The defence will be organised via remote technology (Zoom). Link to the event

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The doctoral thesis will be publicly displayed 10 days before the defence in the Aaltodoc publication archive of Aalto University.

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