Hamann, A. | bioRxiv Science

bioRxiv2020

Prevention of EAE by PEGylated Antigenic Peptide

The treatment of autoimmune disorders such as multiple sclerosis (MS) so far relies largely on the use of non-specific immunosuppressive drugs, which are not able to cure the disease. Presently, approaches to induce antigen-specific tolerance e.g. by peptide-based tolerogenic "inverse" vaccines regain interest. We previously have shown that coupling of peptides to carriers can enhance their capacity to induce regulatory T cells in vivo. We here investigated in an experimental autoimmune encephalomyelitis (EAE) model for chronic MS (MOG C57BL/6) whether the tolerogenic potential of immunodominant myelin T cell epitopes can be improved by conjugation to the synthetic carrier polyethylene glycol (PEG). Indeed, preventive administration of the PEGylated antigenic peptide could almost completely protect mice from EAE development, which was accompanied by reduced immune cell infiltration in the central nervous system (CNS). Depletion of Tregs abrogated the protective effect indicating that Tregs play a crucial role in induction of antigen-specific tolerance in EAE. Treatment during the acute phase was safe and did not induce immune activation. However, treatment at the peak of disease was not affecting the disease course, suggesting that either induction of Tregs is not occurring in the highly inflamed situation, or that the immune system is refractory to regulation in this condition. Thus, PEGylation of antigenic peptides is an effective and feasible strategy to improve tolerogenic (Treg-inducing) peptide-based vaccines, but application in overt disease might require modifications or combination therapies that simultaneously suppress effector mechanisms.

immunology↗

bioRxiv2016

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

bioinformatics↗