Genome-Wide Integration on Transcription Factors, Histone Acetylation and Gene Expression Reveals Genes Co-Regulated by Histone Modification Patterns

N-terminal tails of H2A, H2B, H3 and H4 histone families are subjected to posttranslational modifications that take part in transcriptional regulation mechanisms, such as transcription factor binding and gene expression. Regulation mechanisms under control of histone modification are important but remain largely unclear, despite of emerging datasets for comprehensive analysis of histone modification. In this paper, we focus on what we call genetic harmonious units (GHUs), which are co-occurring patterns among transcription factor binding, gene expression and histone modification. We present the first genome-wide approach that captures GHUs by combining ChIP-chip with microarray datasets from Saccharomyces cerevisiae. Our approach employs noise-robust soft clustering to select patterns which share the same preferences in transcription factor-binding, histone modification and gene expression, which are all currently implied to be closely correlated. The detected patterns are a well-studied acetylation of lysine 16 of H4 in glucose depletion as well as co-acetylation of five lysine residues of H3 with H4 Lys12 and H2A Lys7 responsible for ribosome biogenesis. Furthermore, our method further suggested the recognition of acetylated H4 Lys16 being crucial to histone acetyltransferase ESA1, whose essential role is still under controversy, from a microarray dataset on ESA1 and its bypass suppressor mutants. These results demonstrate that our approach allows us to provide clearer principles behind gene regulation mechanisms under histone modifications and detect GHUs further by applying to other microarray and ChIP-chip datasets. The source code of our method, which was implemented in MATLAB (http://www.mathworks.com/), is available from the supporting page for this paper: http://www.bic.kyoto-u.ac.jp/pathway/natsume/hm_detector.htm

Classification of Promoters Based on the Combination of Core Promoter Elements Exhibits Different Histone Modification Patterns.

Four different histones (H2A, H2B, H3, and H4; two subunits each) constitute a histone octamer, around which DNA wraps to form histone-DNA complexes called nucleosomes. Amino acid residues in each histone are occasionally modified, resulting in several biological effects, including differential regulation of transcription. Core promoters that encompass the transcription start site have well-conserved DNA motifs, including the initiator (Inr), TATA box, and DPE, which are collectively called the core promoter elements (CPEs). In this study, we systematically studied the associations between the CPEs and histone modifications by integrating the Drosophila Core Promoter Database and time-series ChIP-seq data for histone modifications (H3K4me3, H3K27ac, and H3K27me3) during development in Drosophila melanogaster via the modENCODE project. We classified 96 core promoters into four groups based on the presence or absence of the TATA box or DPE, calculated the histone modification ratio at the core promoter region, and transcribed region for each core promoter. We found that the histone modifications in TATA-less groups were static during development and that the core promoters could be clearly divided into three types: i) core promoters with continuous active marks (H3K4me3 and H3K27ac), ii) core promoters with a continuous inactive mark (H3K27me3) and occasional active marks, and iii) core promoters with occasional histone modifications. Linear regression analysis and non-linear regression by random forest showed that the TATA-containing groups included core promoters without histone modifications, for which the measured RNA expression values were not predictable accurately from the histone modification status. DPE-containing groups had a higher relative frequency of H3K27me3 in both the core promoter region and transcribed region. In summary, our analysis showed that there was a systematic link between the existence of the CPEs and the dynamics, frequency and influence on transcriptional activity of histone modifications

The variable importance of each histone modification in determining RNA expression levels was obtained by random forest as mean decrease in node impurity.

<p>The matrices were normalized such that their sum was 1.</p

Hypothetical model of the function of the TATA box.

<p>The green bar represents the transcribed region, and the dotted lines represent the 5′ terminal of the core promoter, +1 position (TSS), and 3′ terminal of the core promoter, from left to right. (A) Model of TATA-less core promoters. TATA-less core promoters exhibit reduced temporal changes during development and could be grouped into three types: core promoters with continuous active marks, those with occasional histone modifications (void), and those with continuous inactive marks. Because core promoters of the first type do not have inactive marks, their RNA expression values can be predicted by the status of the active marks alone. (B) Model of TATA-containing core promoters. TATA-containing core promoters exhibit increased temporal changes during development and could be grouped into two types: core promoters whose RNA expression values are dependent on the status of histone modifications, and core promoters whose RNA expression values are uniform, despite the void state of histone modifications. Since core promoters of the first type have both of active and inactive marks, all of the histone modification statuses are informative for prediction of their RNA expression values.</p

The relative importance of each histone modification in determining RNA expression levels was calculated using the regression equations obtained by linear regression analysis using the LMG method [12].

<p>Values were normalized such that the sum of all values was 1.</p

Diagram of histone modification ratios.

<p>The green bar represents the transcribed region, and the dotted lines represent the 5′ terminal of the core promoter, +1 position (TSS), 3′ terminal of the core promoter, and transcription end site (TES), from left to right. (A) Histone modification ratios at the core promoter region. The pink bar represents the region with H3K4me3, the orange bar represents the region with H3K27ac, and the purple bar represents the region with H3K27me3 within the core promoter region. The ratios of these bars to the area of the core promoter region filled with light blue were calculated. (B) Histone modification ratios at the transcribed region. The red bar represents the region with H3K4me3, the orange bar represents the region with H3K27ac, and the purple bar represents the region with H3K27me3 within the transcribed region. The ratios of these bars to the area of the core promoter region filled with light blue were calculated.</p

Comparison of the histone modification dependency of RNA expression values by linear regression.

<p>Ten-fold cross validation was performed to check that the regression equations reflected the general relationship between the histone modification ratio and the measured log(FPKM) obtained by RNA-seq. The correlation between the measured and predicted log(FPKM) has been represented by a scatterplot. (A) Scatterplot between the measured and predicted log(FPKM) in the Inr group (<i>n</i> = (24 <i>core promoters</i>) × (11 <i>developmental stages</i>) = 264). The correlation coefficient was <i>r</i> = 0.593. (B) Scatterplot between the measured and predicted log(FPKM) in the DPE group (<i>n</i> = (25 <i>core promoters</i>) × (11 <i>developmental stages</i>) = 275). The correlation coefficient was <i>r</i> = 0.613. (C) Scatterplot between the measured and predicted log(FPKM) in the TATA group (<i>n</i> = (33 <i>core promoters</i>) × (11 <i>developmental stages</i>) = 363). The correlation coefficient was <i>r</i> = 0.465. (D) Scatterplot between the measured and predicted log(FPKM) in the TATA-DPE group (<i>n</i> = (14 <i>core promoters</i>) × (11 <i>developmental stages</i>) = 154). The correlation coefficient was <i>r</i> = 0.357. The TATA and TATA-DPE groups were divided into two types based on the distributions in the scatterplots. (E) Scatterplot between the measured and predicted log(FPKM) in the TATAp group (n = 189, for details see the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151917#sec007" target="_blank">Methods</a>). The correlation coefficient was <i>r</i> = 0.533. (F) Scatterplot between the measured and predicted log(FPKM) in the TATA-DPEp group (n = 51, for details see the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151917#sec007" target="_blank">Methods</a>). The correlation coefficient was <i>r</i> = 0.245.</p

Histone modification dynamics in each CPE group.

<p>The y-axis represents genes to which the core promoters were assigned, and the x-axis represents histone modifications (H3K4me3, H3K27ac, and H3K27me3 from left to right for each histone modification) from embryos to adults. E0h: embryo at 0–4 h; E4h: embryo at 4–8 h; E8h: embryo at 8–12 h; E12h: embryo at 12–16 h; E16h: embryo at 16–20 h; E20h: embryo at 20–24 h; L1: larval stage 1; L2: larval stage 2; L3: larval stage 3; Pupae; Male: adult male. The order of genes reflects the results of hierarchical clustering using the pheatmap package in R. The warm color indicates that the histone modification ratio was high. (A) Heatmap of histone modification dynamics in Inr group (n = 24), (B) Heatmap of histone modification dynamics in DPE group (n = 25), (C) Heatmap of histone modification dynamics in TATA group (n = 33), (D) Heatmap of histone modification dynamics in TATA-DPE group (n = 14).</p

Mouse tissue glycome atlas 2022 highlights inter-organ variation in major N-glycan profiles

This study presents mouse tissue glycome atlas representing the profiles of major N-glycans of mouse glycoproteins that may define their essential functions in the surface glycocalyx of mouse organs/tissues and serum-derived extracellular vesicles (exosomes). Cell surface glycocalyx composed of a variety of N-glycans attached covalently to the membrane proteins, notably characteristic N-glycosylation patterns of the glycocalyx, plays a critical role for the regulation of cell differentiation, cell adhesion, homeostatic immune response, and biodistribution of secreted exosomes. Given that the integrity of cell surface glycocalyx correlates significantly with maintenance of the cellular morphology and homeostatic immune functions, dynamic alterations of N-glycosylation patterns in the normal glycocalyx caused by cellular abnormalities may serve as highly sensitive and promising biomarkers. Although it is believed that inter-organs variations in N-glycosylation patterns exist, information of the glycan diversity in mouse organs/tissues remains to be elusive. Here we communicate for the first-time N-glycosylation patterns of 16 mouse organs/tissues, serum, and serum-derived exosomes of Slc:ddY mice using an established solid-phase glycoblotting platform for the rapid, easy, and high throughput MALDI-TOFMS-based quantitative glycomics. The present results elicited occurrence of the organ/tissue-characteristic N-glycosylation patterns that can be discriminated to each other. Basic machine learning analysis using this N-glycome dataset enabled classification between 16 mouse organs/tissues with the highest F1 score (69.7-100%) when neural network algorithm was used. A preliminary examination demonstrated that machine learning analysis of mouse lung N-glycome dataset by random forest algorithm allows for the discrimination of lungs among the different mouse strains such as the outbred mouse Slc:ddY, inbred mouse DBA/2Crslc, and systemic lupus erythematosus model mouse MRL-lpr/lpr with the highest F1 score (74.5-83.8%). Our results strongly implicate importance of human organ/tissue glycome atlas for understanding the crucial and diversified roles of glycocalyx determined by the organ/tissue-characteristic N-glycosylation patterns and the discovery research for N-glycome-based disease-specific biomarkers and therapeutic targets