Biological systems encompass various molecular entities such as genes, proteins, and other biological molecules, including interactions among those components. Understanding a given phenotype, the functioning of a cell or tissue, aetiology of disease, or cellular organization, requires accurate measurements of the abundance profiles of these molecular entities in the form of biomedical data. The analysis of the interplay between these different entities at various levels represented in the form of biological network provides a mechanistic understanding of the observed phenotype. In order to study this interplay, there is a requirement of a conceptual and intuitive framework which can model multiple omics such as genome, transcriptome, or a proteome. This can be addressed by application of network-based strategies.

Translational bioinformatics deals with the development of analytic and interpretive methods to optimize the transformation of different omics and clinical data to understanding of complex diseases and improving human health. Complex diseases such as multiple sclerosis (MS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and non-small cell lung cancer (NSCLC) etc., are hypothesized to be a result of a disturbance in the omic networks rendering the healthy cells to be in a state of malfunction. Even though there are numerous methods to layout the relation of the interactions among omics in complex diseases, the output network modules were not clearly interpreted.

In this PhD thesis, we showed how different omic data such as transcriptome and methylome can be mapped to the network of interactions to extract highly interconnected gene sets relevant to the disease, so called disease modules. First, we selected common module identification methods and assembled them into a unified framework of the methods implemented in an Rpackage MODifieR (Paper I). Secondly, we showed that the concept of the network modules can be applied on the whole genome sequencing data for developing a tested model for predicting myelosuppressive toxicity (Paper II).

Furthermore, we demonstrated that network modules extracted using the methylome data helped identifying several genes that were associated with pregnancy-induced pathways and were enriched for disease-associated methylation changes that were also shared by three auto-immune and inflammatory diseases, namely MS, RA, and SLE (Paper III). Remarkably, those methylation changes correlated with the expected outcome from clinical experience in those diseases. Last, we benchmarked the omic network modules on 19 different complex diseases using both transcriptomic and methylomic data. This led to the identification of a multi-omic MS module that was highly enriched disease-associated genes identified by genome-wide association studies, but also genes associated with the most common environmental risk factors of MS (Paper IV).

The application of the network modules concept on different omics is the centrepiece of the research presented in this PhD thesis. The thesis represents the application of omic network modules in complex diseases and how these modules should be integrated and interpreted. In particular, it aimed to show the importance of networks owing to the incomplete knowledge of the genes dysregulated in complex diseases and the contribution of this thesis that provides tools and benchmarks for the methods as well as insights into how a network module can be extracted and interpreted from the omic data in complex diseases.

Biologiska system består av gener, proteiner och andra biologiska molekyler, liksom interaktioner mellan dessa komponenter. Förståelse av en given fenotyp, funktion av en cell eller vävnad, etiologi av sjukdomar eller cellulär organisation kräver exakta mätningar av uttrycksprofilerna för dessa molekyler, vilket ger upphov till enorma mängder av biomedicinska data. Analys av biomedicinska data tillåter oss att förklara viktiga funktioner i interaktionerna som leder till en mekanistisk förståelse av den observerade fenotypen. Samspelet mellan olika komponenter på olika nivåer kan representeras i form av biologiska nätverk, till exempel protein-protein interaktioner (PPI). Nätverk ger en konceptuell och intuitiv ram för att modellera olika komponenter i flera omik-data, såsom transkriptom. De topologiska egenskaperna hos sjukdomsassocierade gener varierar signifikant från sjukdom till sjukdom.

Translationell bioinformatik handlar om utveckling av analytiska och tolkningsmetoder för att omvandla omik-data till förståelsen av komplexa sjukdomar. Komplexa sjukdomar som multipel skleros, reumatoid artrit och lungcancer är några av de sjukdomar som antas vara resultat av underliggande störningar i omik nätverken. Även om det finns många metoder för att modellera interaktioner mellan omik-data vid komplexa sjukdomar saknas det fortfarande tydlighet i hur de resulterande nätverksmodulerna ska tolkas.

I denna doktorsavhandling visade vi hur olika omik-data som transkriptom och metylom kan användas överlagrat på nätverket av proteininteraktioner och att extrahera tätt sammankopplade nätverksstrukturer av relevans för sjukdom, så kallade sjukdomsmoduler. I den första artikeln gjorde vi ett urval av de mest förekommande metoder för identifiering av sjukdomsmoduler och implementerade dessa i ett R-paket MODifieR, som erbjuder en lättanvänd gemensam struktur för olika metoder, samt möjlighet att kombinera moduler från olika metoder. I den andra artikeln visade vi hur nätverksmodulskoncept kan tillämpas på data från helgenomsekvensering för att utveckla en modell för prediktion av myelosuppressiv toxicitet i icke-småcellig lungcancer.

I tredje artikeln demonstrerades ytterligare en framgångsrik tillämning av nätverksmoduler som användes för att identifiera gener som är associerade med biologiska "pathways" samt sjukdomsassocierade metyleringsförändringar relaterade till multipel skleros, reumatoid artrit och systemisk lupus erythematosus, där sjukdomskopplingar till graviditet undersöktes.

Sedan utvärderades de omiska nätverksmodulerna på 19 olika komplexa sjukdomar genom att använda både transkriptom och metylom data. Vidare identifierade vi också en multi-omik modul i multipel skleros, med signifikant koppling till sjukdomsriskfaktorer genom att utnyttja genomisk överensstämmelse, dvs att flera omik ska ge höga genöverlapp.

Tillämpningen av nätverksmodulerna som ett koncept för att koppla omikdata till sjukdomsmekanismer är kärnan i forskningen som presenteras i denna doktorsavhandling. I synnerhet syftade den till att visa betydelse av hur nätverksomik-koncept kan bidra till kunskap om gener som är dysreglerade vid komplexa sjukdomar för att förstå sjukdomsmekanismer. Denna avhandling ger också verktyg och riktmärken för metoder och insikter i hur en nätverksmodul kan extraheras och tolkas från omik-data vid komplexa sjukdomar.

Cellular organization is believed to be modular, meaning cellular functions are carried out by modules composed of clusters of genes, proteins and metabolites that are interconnected, co-regulated or physically interacting. In turn, these modules interact together and thereby form complex networks that taken together is considered to be the interactome. 

Modern high-throughput biological techniques have made high-scale accurate quantification of these biological molecules possible, the so called omics. The simultaneous measurement of these molecules enables a picture of the state of a cell at a resolution that was never before possible. Mapping these measurements aids greatly to elucidate a network structure of interactions. The ever growing size of public repositories for omics data has ushered in the advent of biology as a (big) data science and opens the door for data hungry machine learning approaches in biology. 

Complex diseases are multi-factorial and arise from a combination of genetic, environmental and lifestyle factors. Additionally, diagnosis and treatment is complicated by the fact that these genetic, environmental and lifestyle factors can vary between patients and may or may not give rise to different disease phenotypes that still classify as the same disease. Genetically, there is substantial heterogeneity among patients and therefore the emergence of a disease phenotype cannot be attributed to a single genetic mutation but rather to a combination of various mutations that may vary from patient to patient. As complex diseases can have different root causes but give rise to a similar disease phenotype, the implication is that different root causes perturb similar components in the interactome. Most of the work in this thesis is aimed at developing methods and computational pipelines to identify, analyze and evaluate these perturbed disease specific sub-networks in the interactome, so called disease modules. 

We started by collecting popular disease module inference methods and combined them in a unified framework, an R package called MODifieR (Paper I). The package uses standardized inputs and outputs, allowing for a more user-friendly way of running multiple disease module inference methods and the combining of modules. Next, we benchmarked the MODifieR methods on a compendium of transcriptomic and methylomic datasets and combined transcriptomic and methylomic disease modules for Multiple Sclerosis (MS) to a highly disease-relevant module greatly enriched with known risk factors for MS (Paper II). Subsequently, we extended the functionality of MODifieR with software for transcription factor hub detection in gene regulatory networks in a new framework with a graphical user interface, MODalyseR. We used MODalyseR to find upstream regulators and identified IKZF1 as an important upstream regulator for MS (Paper III). Lastly, we used the growing large-scale repositories of gene expression data to train a Variational Auto Encoder (VAE) to compress and decompress gene expression profiles with the aim of extracting disease modules from the latent space. Utilizing the continues nature of the latent space in VAE’s, we derived the differences in latent space representations between a compendium of complex disease gene expression profiles and matched healthy controls. We then derived disease modules from the decompressed latent space representation of this difference and found the modules highly enriched with disease-associated genes, generally outperforming the gold standard of transcriptomic analysis of diseases, top differentially expressed genes (Paper IV). 

To conclude, the main scientific contribution of this thesis lies in the development of software and methods for improving disease module inference, the evaluation of existing inference methods, the creation of new analysis workflows for multi-omics modules, and the introduction of a deep learning-based approach to the disease module inference toolkit.