Computational analyses of transcriptome and DNA methylation data

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School of Science | Doctoral thesis (article-based) | Defence date: 2024-04-25
Degree programme
80 + app. 96
Aalto University publication series DOCTORAL THESES, 74/2024
Transcriptomics and epigenomics, via DNA methylation, both study regulation of gene expression. Transcriptomics cover both expression of coding genes which lead to protein expression as well as non-coding genes, like microRNAs, which regulate gene expression, as well as alternative gene splicing. DNA methylation, on the other hand, is the addition of a methyl group to DNA, mostly cytosine, that can either repress or enhance gene expression. Analysis of the transcriptome and DNA methylome are performed to better understand development of diseases (and identify biomarkers) and biological processes such as stem cell development. In this thesis, we analyzed transcriptome and DNA methylation datasets to better understand aspects of stem cell regulation and diseases (asthma and Alzheimer's disease) as well as developed methods for analyzing DNA methylation data.Transcriptome analysis was performed on stem cells to elucidate the function of a stem cell-specific gene, POLR3G, and to determine the relationship of the microRNA Let-7 and protein LIN28 in human embryonic stem cells. It was shown that POLR3G functions in stem cell maintenance rather than repression of transcription as most of the differentially expressed genes were downregulated, which included both coding and non-coding genes. Let-7 and LIN28 function in a negative feedback loop in mouse embryonic stem cells. Unlike the assumption that Let-7 and LIN28 function in hESC as in mESC, it was found that both are expressed in pluripotent hESC. DNA methylation analysis was performed in two diseases, Alzheimer's disease and asthma, as well as on stem cells. Blood samples from twins discordant for AD were analyzed. A gene associated with cognitive function, ADARB2, was found to be differentially methylated in both blood and brain samples. Analysis of blood samples from children with atopic and non-atopic asthma and controls showed that genes previously associated with the immune response and asthma, SMAD3 and PTGDS, were found to be differentially methylated in children with atopic and non-atopic asthma, respectively. Analysis of karyotypically abnormal stem cells showed that a gene known to protect cells against DNA damage and oxidative stress, CAT, was found to be hypermethylated. Moreover, CAT was also found to be differentially methylated in publicly available cancer cell line data. Cancer shares the property of self-renewal with stem cells.Two methods were developed for analysis of DNA methylation data. LuxRep identifies differentially methylated loci by modelling the biochemistry of bisulfite sequencing (BS-seq) at the level of individual DNA methylation libraries. It was shown that inclusion of libraries with varying bisulfite conversion rates in methylation analysis increases accuracy of differential methylation detection. LuxHMM uses HMM and Bayesian regression to identify differentially methylated regions and was shown to perform competitively against other published methods.
Supervising professor
Lähdesmäki, Harri, Prof., Aalto University, Department of Computer Science. Finland
Thesis advisor
Lähdesmäki, Harri, Prof., Aalto University, Department of Computer Science. Finland
transcriptome, DNA methylation, bisulfite sequencing
Other note
  • [Publication 1]: Riikka J. Lund, Nelly Rahkonen, Maia Malonzo, Leni Kauko, Maheswara Reddy Emani, Virpi Kivinen, Elisa Närvä, Esko Kemppainen, Asta Laiho, Heli Skottman, Outi Hovatta, Omid Rasool, Matti Nykter, Harri Lähdesmäki and Riitta Lahesmaa. RNA Polymerase III Subunit POLR3G regulates specific subsets of polyA+ and smallRNA transcriptomes and splicing in human pluripotent stem cells. Stem Cell Reports, 8, 5, 1442-1454, 2017.
    DOI: 10.1016/j.stemcr.2017.04.016 View at publisher
  • [Publication 2]: Nelly Rahkonen, Aki Stubb, Maia Malonzo, Sanna Edelman, Maheswara Reddy Emani, Elisa Närvä, Harri Lähdesmäki, Hannele Ruohola-Baker, Riitta Lahesmaa and Riikka Lund. Mature Let-7 miRNAs fine tune expression of LIN28B in pluripotent human embryonic stem cells. Stem Cell Research, 17, 3, 498-503, 2016.
    DOI: 10.1016/j.scr.2016.09.025 View at publisher
  • [Publication 3]: Mikko Konki, Maia Malonzo, Ida K. Karlsson, Noora Lindgren, Bishwa Ghimire, Johannes Smolander, Noora M. Scheinin, Miina Ollikainen, Asta Laiho, Laura L. Elo, Tapio Lönnberg, Matias Röyttä, Nancy L. Pedersen, Jaakko Kaprio, Harri Lähdesmäki, Juha O. Rinne and Riikka J. Lund. Peripheral blood DNA methylation differences in twin pairs discordant for Alzheimer’s disease. Clinical epigenetics, 11, 1, 1-12, 2019.
    DOI: 10.1186/s13148-019-0729-7 View at publisher
  • [Publication 4]: Riikka J. Lund, Maria Osmala, Maia Malonzo, Minna Lukkarinen, Annamari Leino, Jussi Salmi, Sanna Vuorikoski, Riitta Turunen, Tytti Vuorinen, Cezmi Akdis, Harri Lähdesmäki, Riitta Lahesmaa and Tuomas Jartti. Atopic asthma after rhinovirus-induced wheezing is associated with DNA methylation change in the SMAD3 gene promoter. Allergy, 73, 8, 1735-1740, 2018.
    DOI: 10.1111/all.13473 View at publisher
  • [Publication 5]: Mikko Konki, Kalyan Pasumarthy, Maia Malonzo, Annele Sainio, Cristina Valensisi, Mirva Söderström, Maheswara Reddy Emani, Aki Stubb, Elisa Närvä, Bishwa Ghimire, Asta Laiho, Hannu Järveläinen, Riitta Lahesmaa, Harri Lähdesmäki, R. David Hawkins, and Riikka J. Lund. Epigenetic silencing of the key antioxidant enzyme Catalase in karyotypically abnormal human pluripotent stem cells. Scientific reports, 6, 1, 1-8, 2016.
    DOI: 10.1038/srep22190 View at publisher
  • [Publication 6]: Maia H. Malonzo, Viivi Halla-aho, Mikko Konki, Riikka J. Lund and Harri Lähdesmäki . LuxRep: a technical replicate-aware method for bisulfite sequencing data analysis. BMC bioinformatics, 23, 1, 1-19, 2022.
    DOI: 10.1186/s12859-021-04546-1 View at publisher
  • [Publication 7]: Maia H. Malonzo and Harri Lähdesmäki. LuxHMM: DNA methylation analysis with genome segmentation via hidden Markov model. BMC bioinformatics, 24, 1, 1-14, 2023.
    DOI: 10.1186/s12859-023-05174-7 View at publisher