Ellen Færgestad Mosleth

Unravelling the aetiology of type 2 diabetes (T2D) is challenged by heterogeneity caused by differences in obesity, the ratio of visceral to subcutaneous adipose tissue (VAT/SAT), genetic origin, and ethnicity. We hypothesized that the SAT transcriptome across independent adipose-matched cohorts may reveal novel insight. The SAT transcriptome from three adipose-matched human cohorts, all with and without T2D, were analysed and combined into new cross-cohort datasets using a novel approach for data integration (General Effect Modelling, GEM) to identify common transcriptome patterns of T2D across the cohorts. Two cohorts had the phenotype high body mass index (BMI) subjected to bariatric surgery, and one had the phenotype high VAT/SAT ratio without high BMI. The multivariate SAT transcriptome patterns associated with T2D across all cohorts included dysfunctional membranes and down-regulation of fatty acid β-oxidation, white adipose tissue differentiation, and protein folding. An extensive inflammatory pattern was observed under high BMI, independently of T2D, whereas a small set of inflammatory markers reflecting gut microbiota was elevated specifically in T2D. Our interpretation is that the fundamental aetiology of T2D in SAT is failure in oxidative membrane potential, leading to impairment of physiologically important adipose processes.

General Effect Modelling (GEM) is an umbrella over different methods that utilise effects in the analyses of data with multiple design variables and multivariate responses. To demonstrate the methodology, we here use GEM in gene expression data where we use GEM to combine data from different cohorts and apply multivariate analysis of the effects of the targeted disease across the cohorts. Omics data are by nature multivariate, yet univariate analysis is the dominating approach used for such data. A major challenge in omics data is that the number of features such as genes, proteins and metabolites are often very large, whereas the number of samples is limited. Furthermore, omics research aims to obtain results that are generically valid across different backgrounds. The present publication applies GEM to address these aspects. First, we emphasise the benefit of multivariate analysis for multivariate data. Then we illustrate the use of GEM to combine data from two different cohorts for multivariate analysis across the cohorts, and we highlight that multivariate analysis can detect information that is lost by univariate validation.

Despite intensive research, the aetiology of multiple sclerosis (MS) remains unknown. Cerebrospinal fluid proteomics has the potential to reveal mechanisms of MS pathogenesis, but analyses must account for disease heterogeneity. We previously reported explorative multivariate analysis by hierarchical clustering of proteomics data of MS patients and controls, which resulted in two groups of individuals. Grouping reflected increased levels of intrathecal inflammatory response proteins and decreased levels of proteins involved in neural development in one group relative to the other group. MS patients and controls were present in both groups. Here we reanalysed these data and we also reanalysed data from an independent cohort of patients diagnosed with clinically isolated syndrome (CIS), who have symptoms of MS without evidence of dissemination in space and/or time. Some, but not all, CIS patients had intrathecal inflammation. The analyses reported here identified a common protein signature of MS/CIS that was not linked to elevated intrathecal inflammation. The signature included low levels of complement proteins, semaphorin-7A, reelin, neural cell adhesion molecules, inter-alpha-trypsin inhibitor heavy chain H2, transforming growth factor beta 1, follistatin-related protein 1, malate dehydrogenase 1 cytoplasmic, plasma retinol-binding protein, biotinidase, and transferrin, all known to play roles in neural development. Low levels of these proteins suggest that MS/CIS patients suffer from abnormally low oxidative capacity that results in disrupted neural development from an early stage of the disease.

Altered gene expression in pathways relevant to leukaemogenesis, as well as reduced levels of circulating lymphocytes, have been reported in workers that were exposed to benzene concentrations below 1 ppm. In this study, we analysed whole blood global gene expression patterns in a worker cohort with altered levels of T cells and immunoglobulins IgM and IgA at three time points; pre-shift, post-shift (after three days), and post-recovery (12 hours later). Eight benzene exposed tank workers performing maintenance work in crude oil cargo tanks with a mean benzene exposure of 0.3 ppm (range 0.1–0.5 ppm) and five referents considered to be unexposed were examined by gene expression arrays. By using our data as independent validation, we reanalysed selected genes that were reported to be altered from previous studies of workers being exposed to sub-ppm benzene levels Four out of six genes previously proposed as marker genes in chronically exposed workers separated benzene exposed workers from unexposed referents (CLEC5, ACSL1, PRG2, IFNB1). Even better separation of benzene exposed workers and referents was observed for short-term exposure for genes in the Jak-STAT pathway, particularly elevated expression of IL6 and reduced expression of IL19.