Maheswary Muniandy's dissertation reveals the many faces of obesity

Maheswary Muniandy’s thesis entitled "Molecular Effects of Obesity and Related Metabolic Risk Factors – A Transcriptomics and Metabolomics Approach” will be publically examined on Friday, 8 June, with the permission of the Faculty of Medicine of the University of Helsinki. The thesis has focused on studying obesity using omics data and bioinformatics tools.

The global obesity epidemic is worsening in most parts of the world and the implications of obesity regarding both personal health and health-economics are enormous. Understanding the molecular mechanisms leading to and caused by obesity could help in designing personalised treatment to support weight-loss.

The main aim of M.Sc., MBA Maheswary Muniandy’s thesis entitled "Molecular Effects of Obesity and Related Metabolic Risk Factors – A Transcriptomics and Metabolomics Approach” was to gain an understanding of the complex, multifactorial biology behind obesity using bioinformatics tools. 

Maheswary graduated from the University of Helsinki in 2014, with a masters in bioinformatics. Her career path has been far from typical for a medical researcher - before starting her PhD project under the supervision of Dr. Miina Ollikainen and Professor Kirsi Pietiläinen in 2014, she had been working as a project manager in a big Finnish telecommunications company for five years.

Maheswary’s thesis project is also part of Professor Jaakko Kaprio’s Finnish Twin Cohort project entity. The thesis has focused on studying acquired obesity by using monozygotic twins discordant or concordant for obesity. 

In her thesis study, Maheswary has analysed and combined multiple types of “omics” data, including transcriptomics, metabolomics and biochemistry measures.

Choosing the correct methodology and tools to transform heterogeneous data into biological knowledge is especially difficult when different methods on the same data may yield different results, requiring further statistical or biological validation. Bioinformatics experience is thus essential.

- Maheswary Muniandy

The thesis consists of three publications, two of which have already been published and one has been accepted for publication.

In the first and second publication of the thesis, Maheswary compared gene expression data from the adipose tissue of obese and non-obese monozygotic twins. The team identified three subgroups of acquired obesity, each representing different gene expression and clinical profiles. The clinical effect of obesity varied from benign to the dysfunction of both mitochondrial and inflammatory pathways. On the cell-type level, most of the pathways attributed to acquired obesity were shown to originate from the adipocytes.

My thesis highlights the importance of two-stage bioinformatics approach. We first explored patterns in the data in an unrestrictive hypothesis-free manner in order to identify variations in obesity and then identified the molecular effects of obesity using more targeted data modelling techniques.

In the last study, Mahes investigated various adiposity and blood biochemistry measures and their associations with metabolites in the plasma. Using linear regression analysis, she was able to show that high-density lipoprotein cholesterol (HDL-C) had the strongest association with the plasma metabolites analysed, making HDL-C a suitable measure of early changes in metabolic health. The study also confirmed variations in metabolite profiles in obesity and the possibility to use this data in subgrouping people based on their metabolic health.

These studies confirm that not all acquired obesities are the same and that profiling based on clinical traits, metabolomics and gene expression is a feasible means of identifying these subgroups.

Mahes wants to thank her supervisors for the easy-going and supportive working relationships. The complementary expertise of the supervisors has been valuable and smoothened the often hard path to publication – Mahes is among the rare PhD students whose articles have never been rejected!

The hardest part for me has actually been writing the thesis, especially the statistical methods. As a computer scientist, I love 0/1 type of answers but statistics is multi-faceted and offers several possibilities with regards to analysis; to justify the chosen methods, a deep understanding of the data and the end-goal is essential.

When asked about the future plans, Mahes mentioned that she will first focus on teaching her 6-year old son how to read and how to ride a bike. In the long-term, she is interested in finding a position where she could combine her bioinformatics and telecommunications knowledge, possibly in the area of digital health.

The public examination of Maheswary Muniandy’s doctoral dissertation will take place on 8 June at 12 o'clock noon in Lecture Hall 1 at Haartman Institute, Haartmaninkatu 3. The thesis has been supervised by FIMM Group Leader, Dr. Miina Ollikainen (University of Helsinki) and Professor Kirsi Pietiläinen (University of Helsinki). Assistant Professor Sara Hägg (Karolinska Institutet, Sweden) will serve as the opponent and Professor Kirsi Pietiläinen as the custos.

The dissertation is also available in an electronic form.