Where are we in the implementation of tissue-specific epigenetic clocks?

Introduction: DNA methylation clocks presents advantageous characteristics with respect to the ambitious goal of identifying very early markers of disease, based on the concept that accelerated ageing is a reliable predictor in this sense. Methods: Such tools, being epigenomic based, are expected to be conditioned by sex and tissue specificities, and this work is about quantifying this dependency as well as that from the regression model and the size of the training set. Results: Our quantitative results indicate that elastic-net penalization is the best performing strategy, and better so when-unsurprisingly-the data set is bigger; sex does not appear to condition clocks performances and tissue specific clocks appear to perform better than generic blood clocks. Finally, when considering all trained clocks, we identified a subset of genes that, to the best of our knowledge, have not been presented yet and might deserve further investigation: CPT1A, MMP15, SHROOM3, SLIT3, and SYNGR. Conclusion: These factual starting points can be useful for the future medical translation of clocks and in particular in the debate between multi-tissue clocks, generally trained on a large majority of blood samples, and tissue-specific clocks.

CXCR3 chemokine receptor contributes to specific CD8+ T cell activation by pDC during infection with intracellular pathogens.

Chemokine receptor type 3 (CXCR3) plays an important role in CD8+ T cells migration during intracellular infections, such as Trypanosoma cruzi. In addition to chemotaxis, CXCR3 receptor has been described as important to the interaction between antigen-presenting cells and effector cells. We hypothesized that CXCR3 is fundamental to T. cruzi-specific CD8+ T cell activation, migration and effector function. Anti-CXCR3 neutralizing antibody administration to acutely T. cruzi-infected mice decreased the number of specific CD8+ T cells in the spleen, and those cells had impaired in activation and cytokine production but unaltered proliferative response. In addition, anti-CXCR3-treated mice showed decreased frequency of CD8+ T cells in the heart and numbers of plasmacytoid dendritic cells in spleen and lymph node. As CD8+ T cells interacted with plasmacytoid dendritic cells during infection by T. cruzi, we suggest that anti-CXCR3 treatment lowers the quantity of plasmacytoid dendritic cells, which may contribute to impair the prime of CD8+ T cells. Understanding which molecules and mechanisms guide CD8+ T cell activation and migration might be a key to vaccine development against Chagas disease as those cells play an important role in T. cruzi infection control.

Integrative analysis of microRNA and mRNA expression profiles of monocyte-derived dendritic cells differentiation during experimental cerebral malaria.

Heterogeneity and high plasticity are common features of cells from the mononuclear phagocyte system: monocytes (MOs), macrophages, and dendritic cells (DCs). Upon activation by microbial agents, MO can differentiate into MO-derived DCs (MODCs). In previous work, we have shown that during acute infection with Plasmodium berghei ANKA (PbA), MODCs become, transiently, the main CD11b+ myeloid population in the spleen (SP) and once recruited to the brain play an important role in the development of experimental cerebral malaria (ECM). Here, we isolated 4 cell populations: bone marrow (BM) MOs (BM-MOs) and SP-MOs from uninfected mice; BM inflammatory MOs (BM-iMOs) and SP-MODCs from PbA-infected mice and used a system biology approach to a holistic transcriptomic comparison and provide an interactome analysis by integrating differentially expressed miRNAs (DEMs) and their differentially expressed gene targets (DEGs) data. The Jaccard index (JI) was used for gauging the similarity and diversity among these cell populations. Whereas BM-MOs, BM-iMOs, and SP-MOs presented high similarity of DEGs, SP-MODCs distinguished by showing a greater number of DEGs. Moreover, functional analysis identified an enrichment in canonical pathways, such as DC maturation, neuroinflammation, and IFN signaling. Upstream regulator analysis identified IFNγ as the potential upstream molecule that can explain the observed DEMs-Target DEGs intersections in SP-MODCs. Finally, directed target analysis and in vivo/ex vivo assays indicate that SP-MODCs differentiate in the SP and IFNγ is a main driver of this process.

Evaluation of pre-processing on the meta-analysis of DNA methylation data from the Illumina HumanMethylation450 BeadChip platform.

INTRODUCTION: Meta-analysis is a powerful means for leveraging the hundreds of experiments being run worldwide into more statistically powerful analyses. This is also true for the analysis of omic data, including genome-wide DNA methylation. In particular, thousands of DNA methylation profiles generated using the Illumina 450k are stored in the publicly accessible Gene Expression Omnibus (GEO) repository. Often, however, the intensity values produced by the BeadChip (raw data) are not deposited, therefore only pre-processed values -obtained after computational manipulation- are available. Pre-processing is possibly different among studies and may then affect meta-analysis by introducing non-biological sources of variability. MATERIAL AND METHODS: To systematically investigate the effect of pre-processing on meta-analysis, we analysed four different collections of DNA methylation samples (datasets), each composed of two subsets, for which raw data from controls (i.e. healthy subjects) and cases (i.e. patients) are available. We pre-processed the data from each dataset with nine among the most common pipelines found in literature. Moreover, we evaluated the performance of regRCPqn, a modification of the RCP algorithm that aims to improve data consistency. For each combination of pre-processing (9 × 9), we first evaluated the between-sample variability among control subjects and, then, we identified genomic positions that are differentially methylated between cases and controls (differential analysis). RESULTS AND CONCLUSION: The pre-processing of DNA methylation data affects both the between-sample variability and the loci identified as differentially methylated, and the effects of pre-processing are strongly dataset-dependent. By contrast, application of our renormalization algorithm regRCPqn: (i) reduces variability and (ii) increases agreement between meta-analysed datasets, both critical components of data harmonization.