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ABSTRACT Chromatin is more than a simple genome packaging system, and instead locally distinguished by histone post-translational modifications (PTMs) that can directly change nucleosome structure and / or be “read” by chromatin-associated proteins to mediate downstream events. An accurate understanding of histone PTM binding preference is vital to explain normal function and pathogenesis, and has revealed multiple therapeutic opportunities. Such studies most often use histone peptides, even though these cannot represent the full regulatory potential of nucleosome context. Here we apply a range of complementary and easily adoptable biochemical and genomic approaches to interrogate fully defined peptide and nucleosome targets with a diversity of mono or multivalent chromatin readers. In the resulting data, nucleosome context consistently refined reader binding, and multivalent engagement was more often regulatory than simply additive. This included abrogating the binding of the Polycomb group L3MBTL1 MBT to histone tails with lower methyl states (me1 or me2 at H3K4, H3K9, H3K27, H3K36 or H4K20); and confirmation that the CBX7 chromodomain and AT-hook-like motif (CD-ATL) tandem act as a functional unit to confer specificity for H3K27me3. Further,in vitronucleosome preferences were confirmed byin vivoreader-CUT&RUN genomic mapping. Such data confirms that more representative chromatin substrates provide greater insight to biological mechanism and its disorder in human disease. 

Abstract The human brain is a complex organ that consists of several regions each with a unique gene expression pattern. Our intent in this study was to construct a gene co-expression network (GCN) for the normal brain using RNA expression profiles from the Genotype-Tissue Expression (GTEx) project. The brain GCN contains gene correlation relationships that are broadly present in the brain or specific to thirteen brain regions, which we later combined into six overarching brain mini-GCNs based on the brain’s structure. Using the expression profiles of brain region-specific GCN edges, we determined how well the brain region samples could be discriminated from each other, visually with t-SNE plots or quantitatively with the Gene Oracle deep learning classifier. Next, we tested these gene sets on their relevance to human tumors of brain and non-brain origin. Interestingly, we found that genes in the six brain mini-GCNs showed markedly higher mutation rates in tumors relative to matched sets of random genes. Further, we found that cortex genes subdivided Head and Neck Squamous Cell Carcinoma (HNSC) tumors and Pheochromocytoma and Paraganglioma (PCPG) tumors into distinct groups. The brain GCN and mini-GCNs are useful resources for the classification of brain regions and identification of biomarker genes for brain related phenotypes. 

Bigenic expression relationships are conventionally defined based on metrics such as Pearson or Spearman correlation that cannot typically detect latent, non-linear dependencies or require the relationship to be monotonic. Further, the combination of intrinsic and extrinsic noise as well as embedded relationships between sample sub-populations reduces the probability of extracting biologically relevant edges during the construction of gene co-expression networks (GCNs). In this report, we address these problems via our NetExtractor algorithm. NetExtractor examines all pairwise gene expression profiles first with Gaussian mixture models (GMMs) to identify sample sub-populations followed by mutual information (MI) analysis that is capable of detecting non-linear differential bigenic expression relationships. We applied NetExtractor to brain tissue RNA profiles from the Genotype-Tissue Expression (GTEx) project to obtain a brain tissue specific gene expression relationship network centered on cerebellar and cerebellar hemisphere enriched edges. We leveraged the PsychENCODE pre-frontal cortex (PFC) gene regulatory network (GRN) to construct a cerebellar cortex (cerebellar) GRN associated with transcriptionally active regions in cerebellar tissue. Thus, we demonstrate the utility of our NetExtractor approach to detect biologically relevant and novel non-linear binary gene relationships.