Tudor

A skeleton analysis method was developed to quantify microglia morphology in immunofluorescent images of fixed brain tissue. Confocal images (21-μm z-stack at 3-μm intervals, Zeiss 510, 40×/1.3 oil objective) were acquired at each ipsilateral and contralateral region as identified in Figure 1A. For skeleton analysis, the maximum intensity projection of the iba-1 positive channel was enhanced to visualize all microglia processes followed by noise de-speckling to eliminate single-pixel background fluorescence. The resulting image was converted to a binary and then skeletonized using Image J software (Figure 1B). The AnalyzeSkeleton plugin (http://imagejdocu.tudor.lu/) was then applied to all skeletonized images to collect data on the number of endpoints per frame (Figure 1B, blue) and process length (Figure 1B, orange). These data were used as measures of microglia morphology based on previous reports showing reduced microglia process branching complexity and process length in response to injury [14 (link)-16 (link)]. In addition, others have assessed the microglia process length of single cells using a similar type of analysis [16 (link)]. The number of cell somas per frame was used to normalize all process endpoints and process lengthes.
Confocal images were acquired from an additional cohort of slices as described above in ipsilateral and matching contralateral regions. Using Image J, the minimum threshold (0–255) was adjusted for each contralateral image to exclude background fluorescence (average minimum across all images was 18.5 ± 5); thresholding values were constant between matching contralateral and ipsilateral regions. The percent area and mean fluorescence intensity for each threshold image were multiplied to result in the total fluorescence intensity (TFI) for each image. Cells were counted in each image to result in TFI/cell.

The genomic regions were defined as chromosome arms and centers based on previous works^{12 (link),83 (link),84 (link)}. Briefly, recombination rates and H3K9 methylation enrichment were used to estimate the physical boundaries between the arms and central regions of each chromosome. Chromosome arms were characterized by high levels of recombination rate and H3K9 methylation. Coordinates of arms and center regions of each chromosome were shown in Supplementary Table 4.
Heterochromatin was defined by occupancy of H3K9me2, H3K9me3, and H3K27me3, and euchromatin was determined by covering of H3K4me3, H3K36me3, and H3K79me3^{12 (link),85 (link)–88 (link)}. Association between chromodomain proteins and histone modifications was detected by correlation, heatmap, and peak overlapping analysis.
For correlation analysis, combined peaks were determined by peaks called from any of the chromodomain protein or histone modification ChIP-seq datasets. The overlapping peaks were merged (n = 42,496). Then the merged peaks were divided into chromosome arm (n = 24,284) and center peaks (n = 18,212). The 95th percentile values were extracted from bigWig files using the Python package pyBigWig over each chromosome arm and center peak. The values were normalized to input signals, logarithm-transformed, and then standardized to Z scores. Correlation coefficients were calculated by the cor [Pearson] function in R using the treated values. A two-sided t test was performed.
For heatmap analysis in Fig. 2b–e, Supplementary Figs. 8d–g, 9a, the called peaks of each chromodomain proteins were used. Deeptools subcommand computeMatrix (version 3.4.3) was used to calculate score matrix for heatmap with defined parameters (reference-point --referencePoint center -b 3000 -a 3000 --skipZeros). The heatmap was plotted with Deeptools subcommand plotHeatmap (version 3.5.0). To identify heterogeneity of chromatin states of chromodomain protein targets, heatmap of each chromodomain protein was clustered. Parameter --kmeans was used to define the number of clusters. For each chromodomain protein, --kmeans 1, 2, and 3 were all applied. Plots displaying no heterogeneity of chromatin states within each cluster were selected. A plot with minimum kmeans number for each chromodomain protein from the selected plots was shown.
Significant overlapped peaks of chromodomain proteins and histone modifications were defined by using IntervalStats software package^{89 (link)}. The method compared each single peak region from a ‘query’ experiment to the set of peak regions in a ‘reference’ experiment. P-values represents the significance of query peak proximity to the reference peak. Pairs of peaks were considered significantly overlapped with P < 0.05. Both chromodomain proteins and histone modifications were used as queries and references (Fig. 2a, Supplementary Fig. 8a–c). The percentage of overlap was listed in Supplementary Table 5. We used the percentage of PTM-covered peaks of each chromodomain protein for “graded histone occupied” evaluation. For many chromodomain proteins, PTM covered more than 30% of their peaks. However, for PTMs, chromodomain proteins rarely covered more than 30% (Supplementary Fig. 8b, c, Supplementary Table 5). We speculated that for a given PTM, there are a number of additional readers besides chromodomain proteins, such as Tudor and MBT, etc., may function redundantly to recognize the PTM.
“Graded histone modification occupied” for a pair of chromodomain protein and histone modification fell into 3 categories: grade 3 = “prominent”, grade 2 = “detectable”, and grade 1 = “weak”. Grade 3 was defined as pairs meeting all of the 3 criteria: 1. “Pearson correlation coefficient (r) ≥ 0.300”. 2. “ ≥30% of significant peaks occupied by a certain histone modification (using IntervalStats software package with P < 0.05)”. 3. “Overlap detected by heatmap analysis”. Grade 2 meets 2 out of the 3 criteria and grade 1 meets any of the 3. For example, on chromosome arms, Pearson correlation coefficient of CEC-5 GFP(A) and H3K9me2, r = 0.333 (Supplementary Fig. 10); 44.3% peaks of CEC-5 were significantly occupied by H3K9me2 (Fig. 2a and Supplementary Table 5); Heatmap analysis found 1585 out of 1854 targets of CEC-5 were covered by H3K9me2 (Fig. 2b). Therefore, the association of CEC-5 and H3K9me2 on chromosome arms was classified as “grade 3 = prominent” (Fig. 2f and Supplementary Table 6).

Sensor-based measures of physical activity and fitness measures include daily steps, energy expenditure, activity intensity and variety, physical inactivity, and BMI.

□ Steps: Accelerometer-based measurement of steps (steps/day) will be recorded, and participants will be categorized as low active (<5000 steps/day), moderately active (5000–10,000 steps/day), and highly active (>10,000 steps/day) adapted from the Tudor-Locke & Bassett [93 (link)] framework (originally: sedentary (<5,000 steps/day), low active (5000 to 7499 steps/day), somewhat active (7500 to 9999 steps/day), active (10,000 to 12,499 steps/day), and highly active (>12,500 steps/day)).

□ Total daily energy expenditure: Based on the work of Livesey [94 (link)], daily activity will be computed as the discrepancy from the individual basal metabolic rate (BMR; MJ/day) requirements determined by age, sex, and body weight (kilograms). Participants with total daily energy expenditure <1.5, BMR are considered low active, 1.5–1.7 BMR reflects moderate daily activity, and daily energy expenditure >1.7 BMR will be treated as highly active.

□ Activity variety: Metabolic equivalent of task (MET) minutes/day will be assessed for daily activities [95 ].

□ Activity intensity: Activity intensity will be computed as mean heart rate in relation to minimum and maximum rate during the measurement period.

□ Physical inactivity: Physical inactivity will be assessed as the number of minutes per days those participants exhibit waking inactivity (sitting, standing) Classification is as follows: <60 min/day = good, 60–240 min/day = moderate, >240 min/day = poor. Also, inactivity disruptions will be measured, operationalized as a disruption of a ≥ 30-min inactivity period for at least 1 min (e.g., short walk after a 30-min sitting period).

□ BMI: The BMI will be computed (kg/m²). According to the WHO [96 ], participants will be classified as underweight (<18.5 kg/m²), normal weight (18.5 to 25 kg/m²), and overweight (>25 kg/m²). Height and weight are assessed by questionnaire.

For the induction of adipocyte differentiation, BmN4 cells were cultured in IPL-41 medium supplemented with 10% fetal bovine serum, 10 μg/ml insulin (SIGMA), 0.5 μM 3-isobutyl-1-methylxanthine (SIGMA) and 250 μM dexamethasone (SIGMA). Medium was changed twice a week and collected 10 and 20 days after the drug addition to prepare RNA sequence libraries. These libraries were mapped to the silkworm genome and genemodel, transposon, and the novel inserts identified in this paper using Hisat2 with the default parameters, and the mapped reads were calculated by coverageBed of bedtools [41 (link), 49 (link)]. Silkworm homologs of piRNA-related genes were identified using blast of silkbase. Since the genemodel for BmAgo3 was splited into three (KWMTBOMO01223-5) and the genemodel for Tudor was splited into two (KWMTBOMO06482-3), the RPKM was calculated by combining them.