DNA Methylation Signatures of Multiple Sclerosis Occur Independently of Known Genetic Risk and Are Primarily Attributed to B Cells and Monocytes.

Epigenetic mechanisms can regulate how DNA is expressed independently of sequence and are known to be associated with various diseases. Among those epigenetic mechanisms, DNA methylation (DNAm) is influenced by genotype and the environment, making it an important molecular interface for studying disease etiology and progression. In this study, we examined the whole blood DNA methylation profiles of a large group of people with (pw) multiple sclerosis (MS) compared to those of controls. We reveal that methylation differences in pwMS occur independently of known genetic risk loci and show that they more strongly differentiate disease (AUC = 0.85, 95% CI 0.82-0.89, p = 1.22 × 10-29) than known genetic risk loci (AUC = 0.72, 95% CI: 0.66-0.76, p = 9.07 × 10-17). We also show that methylation differences in MS occur predominantly in B cells and monocytes and indicate the involvement of cell-specific biological pathways. Overall, this study comprehensively characterizes the immune cell-specific epigenetic architecture of MS.

RNA-sequencing and mass-spectrometry proteomic time-series analysis of T-cell differentiation identified multiple splice variants models that predicted validated protein biomarkers in inflammatory diseases.

Profiling of mRNA expression is an important method to identify biomarkers but complicated by limited correlations between mRNA expression and protein abundance. We hypothesised that these correlations could be improved by mathematical models based on measuring splice variants and time delay in protein translation. We characterised time-series of primary human naïve CD4+ T cells during early T helper type 1 differentiation with RNA-sequencing and mass-spectrometry proteomics. We performed computational time-series analysis in this system and in two other key human and murine immune cell types. Linear mathematical mixed time delayed splice variant models were used to predict protein abundances, and the models were validated using out-of-sample predictions. Lastly, we re-analysed RNA-seq datasets to evaluate biomarker discovery in five T-cell associated diseases, further validating the findings for multiple sclerosis (MS) and asthma. The new models significantly out-performing models not including the usage of multiple splice variants and time delays, as shown in cross-validation tests. Our mathematical models provided more differentially expressed proteins between patients and controls in all five diseases. Moreover, analysis of these proteins in asthma and MS supported their relevance. One marker, sCD27, was validated in MS using two independent cohorts for evaluating response to treatment and disease prognosis. In summary, our splice variant and time delay models substantially improved the prediction of protein abundance from mRNA expression in three different immune cell types. The models provided valuable biomarker candidates, which were further validated in MS and asthma.

Deconvolution of the hematopoietic stem cell microenvironment reveals a high degree of specialization and conservation.

Understanding the regulation of normal and malignant human hematopoiesis requires comprehensive cell atlas of the hematopoietic stem cell (HSC) regulatory microenvironment. Here, we develop a tailored bioinformatic pipeline to integrate public and proprietary single-cell RNA sequencing (scRNA-seq) datasets. As a result, we robustly identify for the first time 14 intermediate cell states and 11 stages of differentiation in the endothelial and mesenchymal BM compartments, respectively. Our data provide the most comprehensive description to date of the murine HSC-regulatory microenvironment and suggest a higher level of specialization of the cellular circuits than previously anticipated. Furthermore, this deep characterization allows inferring conserved features in human, suggesting that the layers of microenvironmental regulation of hematopoiesis may also be shared between species. Our resource and methodology is a stepping-stone toward a comprehensive cell atlas of the BM microenvironment.

Data-driven bioinformatics to disentangle cells within a tissue microenvironment.

Molecular profiling of clinical tissue samples is at the core of precision medicine. Yet, to elucidate the contribution of mixed cell types and detect changes in cell populations in response to infections or drugs is challenging. Recent advances using machine learning promise to learn explanatory models directly from data.

Deep neural network prediction of genome-wide transcriptome signatures - beyond the Black-box.

Prediction algorithms for protein or gene structures, including transcription factor binding from sequence information, have been transformative in understanding gene regulation. Here we ask whether human transcriptomic profiles can be predicted solely from the expression of transcription factors (TFs). We find that the expression of 1600 TFs can explain >95% of the variance in 25,000 genes. Using the light-up technique to inspect the trained NN, we find an over-representation of known TF-gene regulations. Furthermore, the learned prediction network has a hierarchical organization. A smaller set of around 125 core TFs could explain close to 80% of the variance. Interestingly, reducing the number of TFs below 500 induces a rapid decline in prediction performance. Next, we evaluated the prediction model using transcriptional data from 22 human diseases. The TFs were sufficient to predict the dysregulation of the target genes (rho = 0.61, P < 10-216). By inspecting the model, key causative TFs could be extracted for subsequent validation using disease-associated genetic variants. We demonstrate a methodology for constructing an interpretable neural network predictor, where analyses of the predictors identified key TFs that were inducing transcriptional changes during disease.

DNA Transposon Expansion is Associated with Genome Size Increase in Mudminnows.

Genome sizes of eukaryotic organisms vary substantially, with whole-genome duplications (WGD) and transposable element expansion acting as main drivers for rapid genome size increase. The two North American mudminnows, Umbra limi and Umbra pygmaea, feature genomes about twice the size of their sister lineage Esocidae (e.g., pikes and pickerels). However, it is unknown whether all Umbra species share this genome expansion and which causal mechanisms drive this expansion. Using flow cytometry, we find that the genome of the European mudminnow is expanded similarly to both North American species, ranging between 4.5 and 5.4 pg per diploid nucleus. Observed blocks of interstitially located telomeric repeats in U. limi suggest frequent Robertsonian rearrangements in its history. Comparative analyses of transcriptome and genome assemblies show that the genome expansion in Umbra is driven by the expansion of DNA transposon and unclassified repeat sequences without WGD. Furthermore, we find a substantial ongoing expansion of repeat sequences in the Alaska blackfish Dallia pectoralis, the closest relative to the family Umbridae, which might mark the beginning of a similar genome expansion. Our study suggests that the genome expansion in mudminnows, driven mainly by transposon expansion, but not WGD, occurred before the separation into the American and European lineage.

Functional genomics analysis of vitamin D effects on CD4+ T cells in vivo in experimental autoimmune encephalomyelitis ‬.

Vitamin D exerts multiple immunomodulatory functions and has been implicated in the etiology and treatment of several autoimmune diseases, including multiple sclerosis (MS). We have previously reported that in juvenile/adolescent rats, vitamin D supplementation protects from experimental autoimmune encephalomyelitis (EAE), a model of MS. Here we demonstrate that this protective effect associates with decreased proliferation of CD4+ T cells and lower frequency of pathogenic T helper (Th) 17 cells. Using transcriptome, methylome, and pathway analyses in CD4+ T cells, we show that vitamin D affects multiple signaling and metabolic pathways critical for T-cell activation and differentiation into Th1 and Th17 subsets in vivo. Namely, Jak/Stat, Erk/Mapk, and Pi3K/Akt/mTor signaling pathway genes were down-regulated upon vitamin D supplementation. The protective effect associated with epigenetic mechanisms, such as (i) changed levels of enzymes involved in establishment and maintenance of epigenetic marks, i.e., DNA methylation and histone modifications; (ii) genome-wide reduction of DNA methylation, and (iii) up-regulation of noncoding RNAs, including microRNAs, with concomitant down-regulation of their protein-coding target RNAs involved in T-cell activation and differentiation. We further demonstrate that treatment of myelin-specific T cells with vitamin D reduces frequency of Th1 and Th17 cells, down-regulates genes in key signaling pathways and epigenetic machinery, and impairs their ability to transfer EAE. Finally, orthologs of nearly 50% of candidate MS risk genes and 40% of signature genes of myelin-reactive T cells in MS changed their expression in vivo in EAE upon supplementation, supporting the hypothesis that vitamin D may modulate risk for developing MS.

Computational disease modeling - fact or fiction?

BACKGROUND: Biomedical research is changing due to the rapid accumulation of experimental data at an unprecedented scale, revealing increasing degrees of complexity of biological processes. Life Sciences are facing a transition from a descriptive to a mechanistic approach that reveals principles of cells, cellular networks, organs, and their interactions across several spatial and temporal scales. There are two conceptual traditions in biological computational-modeling. The bottom-up approach emphasizes complex intracellular molecular models and is well represented within the systems biology community. On the other hand, the physics-inspired top-down modeling strategy identifies and selects features of (presumably) essential relevance to the phenomena of interest and combines available data in models of modest complexity. RESULTS: The workshop, "ESF Exploratory Workshop on Computational disease Modeling", examined the challenges that computational modeling faces in contributing to the understanding and treatment of complex multi-factorial diseases. Participants at the meeting agreed on two general conclusions. First, we identified the critical importance of developing analytical tools for dealing with model and parameter uncertainty. Second, the development of predictive hierarchical models spanning several scales beyond intracellular molecular networks was identified as a major objective. This contrasts with the current focus within the systems biology community on complex molecular modeling. CONCLUSION: During the workshop it became obvious that diverse scientific modeling cultures (from computational neuroscience, theory, data-driven machine-learning approaches, agent-based modeling, network modeling and stochastic-molecular simulations) would benefit from intense cross-talk on shared theoretical issues in order to make progress on clinically relevant problems.