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Graphical abstract, The brain is a highly complex organ consisting of numerous types of cells with ample diversity at the epigenetic level to achieve distinct gene expression profiles. During neuronal cell specification, transcription factors (TFs) form regulatory modules with chromatin remodeling proteins to initiate the cascade of epigenetic changes. Currently, little is known about brain epigenetic regulatory modules and how they regulate gene expression in a cell-type specific manner. To infer TFs involved in neuronal specification, we applied a recursive motif search approach on the differentially methylated regions identified from single-cell methylomes. The epigenetic transcription regulatory modules (ETRM), including EGR1 and MEF2C, were predicted and the co-expression of TFs in ETRMs were examined with RNA-seq data from single or sorted brain cells using a conditional probability matrix. Lastly, computational predications were validated with EGR1 ChIP-seq data. In addition, methylome and RNA-seq data generated from Egr1 knockout mice supported the essential role of EGR1 in brain epigenome programming, in particular for excitatory neurons. In summary, we demonstrated that brain single cell methylome and RNA-seq data can be integrated to gain a better understanding of how ETRMs control cell specification. The analytical pipeline implemented in this study is freely accessible in the Github repository (https://github.com/Gavin-Yinld/brain_TF).

DNA methylation is an epigenetic modification modulating the structure of DNA molecule and the interactions with its binding proteins. Accumulating large-scale methylation data motivates the development of analytic tools to facilitate methylome data mining. One critical phenomenon associated with dynamic DNA methylation is the altered DNA binding affinity of transcription factors,which plays key roles in gene expression regulation. In this study,we conceived an algorithm to predict epigenetic regulatory modules through recursive motif analyses on differentially methylated loci. A two-step procedure was implemented to first group differentially methylated loci into clusters according to their correlations in methylation profiles and then to repeatedly identify the transcription factor binding motifs significantly enriched in each cluster. Weapplied this tool on methylome datasets generated for mouse brainswhich have a lack of DNA demethylation enzymes TET1 or TET2. Compared with wild type control, the differentially methylated CpG sites identified in TET1 knockout mouse brains differed significantly from those determined for TET2 knockout. Transcription factors with zinc finger DNA binding domains including Egr1, Zic3, and Zeb1 were predicted to be associated with TET1 mediated brain methylome programming, while Lhx family members with Homeobox domains were predicted to be associated with TET2 function. Interestingly, genomic loci from a co-methylated cluster often host motifs for transcription factors sharing the same DNA binding domains. Altogether, our study provided a systematic approach for epigenetic regulatory module identification and will help throw light on the interplay of DNA methylation and transcription factors. This work was supported by NIH grant NS094574 and the Biocomplexity Institute faculty development fund for H.X, and VT's Open Access Subvention Fund.

DNA methylation plays important roles in the regulation of nervous system development and in cellular responses to environmental stimuli such as light-derived signals. Despite great efforts in understanding the maturation and refinement of visual circuits, we lack a clear understanding of how changes in DNA methylation correlate with visual activity in the developing subcortical visual system, such as in the dorsal lateral geniculate nucleus (dLGN), the main retino-recipient region in the dorsal thalamus. Here, we explored epigenetic dynamics underlying dLGN development at ages before and after eye opening in wild-type mice and mutant mice in which retinal ganglion cells fail to form. We observed that development-related epigenetic changes tend to co-localize together on functional genomic regions critical for regulating gene expression, while retinal-input-induced epigenetic changes are enriched on repetitive elements. Enhancers identified in neurons are prone to methylation dynamics during development, and activity-induced enhancers are associated with retinal-input-induced epigenetic changes. Intriguingly, the binding motifs of activity-dependent transcription factors, including EGR1 and members of MEF2 family, are enriched in the genomic regions with epigenetic aberrations in dLGN tissues of mutant mice lacking retinal inputs. Overall, our study sheds new light on the epigenetic regulatory mechanisms underlying the role of retinal inputs on the development of mouse dLGN. Electronic supplementary material The online version of this article (10.1186/s13072-019-0257-x) contains supplementary material, which is available to authorized users.

Additional file 2: Fig. S1. Distribution of read depth for CpG sites determined in four dLGN WGBS libraries. Fig. S2. Distribution of CpG methylation levels determined for four dLGN WGBS libraries. Fig. S3. Venn diagram of DMS lists identified from four pairwise comparisons. Fig. S4. Relationships between mCH and gene expression. The mCH profiles for (A) P6 WT, (B) P6 Math5KO, (C) P23 WT and (D) P23 Math5KO. Red line denote the group of genes with the top one-third expression; green line denote the group of genes with the median one-third expression; blue line denote the group of genes with the bottom one-third expression; and black line show the group of genes not expressed. The average expression levels at P3 and P7 were shown for P6. Fig. S5. Pairwise comparisons identified common sets of 463 upregulated (A) and 554 downregulated (B) genes from P3 to P23 were identified in both WT and Math5KO. No gene was identified to be overlapped for upregulated (C) or downregulated (D) in Math5KO in pairwise comparisons between WT and Math5KO dLGN across all four time points. Fig. S6. The mCH profiles for 463 upregulated (Green) and 554 downregulated (Red) genes from P3 to P23 in P7 WT (A), P7 Math5KO (B), P23 WT (C) and P23 Math5KO methylomes. Fig. S7. Heat map (A) and scatter plot (B) for gene expression profiles of 61 genes which promoters overlapped with DMRs showing methylation increased in P23 Math5KO. Heat map was generated using RNAseq data from four time points with color bar showing log (1 + TPM), and scatter plot was generated with RNAseq data at P23 for WT and Math5KO with X- and Y-axis showing log (1 + TPM). Fig. S8. Methylation profiles of DMRs during mouse brain development. Fig. S9. DNA Methylation for DMRs and gene expression profiles for Lhx2 and CACNA1E loci. Fig. S10. WGCNA clustering and motif enrichment analysis for DMS sites identified in the comparison between P6 WT and P23 WT. (A) WGCNA clustering. (B) Methylation profiles of different clusters. (C) Top TFs with motifs significantly enriched in each cluster predicted by HOMER. Fig. S11. WGCNA clustering and motif enrichment analysis for DMS sites identified in the comparison between P6 Math5KO and P23 Math5KO. (A) WGCNA clustering. (B) Methylation profiles of different clusters. (C) Top TFs with motifs significantly enriched in each cluster predicted by HOMER. Fig. S12. WGCNA clustering and motif enrichment analysis for DMS sites identified in the comparison between P6 WT and P6 Math5KO. (A) WGCNA clustering. (B) Methylation profiles of different clusters. (C) Top TFs with motifs significantly enriched in each cluster predicted by HOMER. Fig. S13. WGCNA clustering and motif enrichment analysis for DMS sites identified in the comparison between P23 WT and P23 Math5KO. (A) WGCNA clustering. (B) Methylation profiles of different clusters. (C) Top TFs with motifs significantly enriched in each cluster predicted by HOMER.

Gene expression regulation is a complex process involving the interplay between transcription factors and chromatin states. Significant progress has been made toward understanding the impact of chromatin states on gene expression. Nevertheless, the mechanism of transcription factors binding combinatorially in different chromatin states to enable selective regulation of gene expression remains an interesting research area. We introduce a nonparametric Bayesian clustering method for inhomogeneous Poisson processes to detect heterogeneous binding patterns of multiple proteins including transcription factors to form regulatory modules in different chromatin states. We applied this approach on ChIP-seq data for mouse neural stem cells containing 21 proteins and observed different groups or modules of proteins clustered within different chromatin states. These chromatin-state-specific regulatory modules were found to have significant influence on gene expression. We also observed different motif preferences for certain TFs between different chromatin states. Our results reveal a degree of interdependency between chromatin states and combinatorial binding of proteins in the complex transcriptional regulatory process. The software package is available on Github at - https://github.com/BSharmi/DPM-LGCP. NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [NS094574]; Biocomplexity Institute of Virginia Tech; VT's Open Access Subvention Fund This work was supported by NIH grant NS094574, the faculty program fund from the Biocomplexity Institute of Virginia Tech to HX, and VT's Open Access Subvention Fund.

This paper presents a novel noncontact vital sign detection (VSD) system, which integrates an entire radar transceiver along with advanced signal processing algorithms in one compact PXI (Peripheral Component Interconnect eXtensions for Instrumentation) system. Realtime signal processing has been implemented using automatic DC calibration based on compressed sensing algorithm and ‘differentiate and cross-multiply’ (DACM) demodulation algorithm. Experimental results show that the proposed compact instrument based radar is able to measure respiration motions with simple hardware configuration. This facilitates many proof-of-concept studies for noncontact vital sign detection.