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Jul 16, 2023

Nature Genetics volume 54, pagine 1919–1932 (2022)Citare questo articolo

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Non è chiaro il motivo per cui l'esaurimento acuto di CTCF (fattore legante CCCTC) e di coesione influenzi solo marginalmente l'espressione della maggior parte dei geni nonostante perturbi sostanzialmente il ripiegamento del genoma tridimensionale (3D) a livello di domini e anelli strutturali. Per risolvere questo enigma, abbiamo utilizzato Micro-C ad alta risoluzione e profilazione della trascrizione nascente nelle cellule staminali embrionali di topo. Troviamo che le interazioni potenziatore-promotore (E-P) sono in gran parte insensibili alla deplezione acuta (3 ore) di CTCF, coesione o WAPL. YY1 è stato proposto come regolatore strutturale dei loop E – P, ma l'esaurimento acuto di YY1 ha avuto anche effetti minimi sui loop E – P, sulla trascrizione e sul ripiegamento del genoma 3D. Sorprendentemente, l'imaging di cellule vive e singole molecole ha rivelato che la deplezione della coesione riduceva il legame del fattore di trascrizione (TF) con la cromatina. Pertanto, sebbene CTCF, coesione, WAPL o YY1 non siano necessari per il mantenimento a breve termine della maggior parte delle interazioni E-P e dell'espressione genica, i nostri risultati suggeriscono che la coesione può facilitare i TF nella ricerca e nel legame dei loro bersagli in modo più efficiente.

I test basati sulla cattura della conformazione cromosomica (Hi-C) ad alto rendimento hanno trasformato la nostra comprensione del ripiegamento del genoma 3D1,2. Sulla base di tali studi, possiamo distinguere almeno tre livelli di ripiegamento del genoma 3D. Innanzitutto, il genoma è segregato nei compartimenti A e B, che corrispondono in gran parte rispettivamente ai segmenti di cromatina attivi e inattivi, e appaiono come uno schema simile a un plaid nelle mappe di contatto Hi-C3. In secondo luogo, le proteine ​​CTCF e la coesione aiutano a ripiegare il genoma in domini topologicamente associati (TAD)4,5 e anelli strutturali di cromatina6, probabilmente attraverso l'estrusione dell'anello del DNA7,8. In terzo luogo, su una scala molto più fine, gli elementi trascrizionali si impegnano in interazioni cromatiniche a lungo raggio come le interazioni E–P e promotore-promotore (P–P) per formare domini locali9,10,11.

Eleganti esperimenti che combinano la deplezione proteica acuta di CTCF, coesione e proteine ​​regolatrici della coesione con Hi-C o approcci di imaging hanno rivelato il ruolo di CTCF e coesione nella regolazione dei primi due livelli: TAD e compartimenti12,13,14,15,16. Tuttavia, Hi-C è inefficace per catturare il terzo livello del ripiegamento del genoma 3D: le interazioni E–P/P–P trascrizionalmente importanti su scala fine9,17,18. La nostra comprensione del ruolo del CTCF e della coesione nella regolazione dell'espressione genica deriva principalmente da esperimenti genetici concentrati su alcuni loci dello sviluppo19,20,21. Pertanto, non era chiaro se, quando, dove e come CTCF/coesina regolasse le interazioni E–P/P–P e l'espressione genica.

Recentemente abbiamo riferito che Micro-C può risolvere efficacemente il ripiegamento ultrafine del genoma 3D alla risoluzione del nucleosoma22,23, comprese le interazioni E–P/P–P9,17. Nel presente studio, abbiamo utilizzato Micro-C, sequenziamento dell'immunoprecipitazione della cromatina (ChIP-seq), sequenziamento dell'RNA totale (RNA-seq) e nascente RNA-seq24 per indagare sistematicamente come CTCF, RAD21 (subunità di coesione), WAPL ( scaricatore di coesione) o YY1 (una presunta proteina strutturale25) influenza le interazioni della cromatina che regolano i geni e la trascrizione nelle cellule staminali embrionali di topo (mESC). Infine, concentrandosi sulle dinamiche di YY1 è stato scoperto un ruolo inaspettato della coesione nel facilitare il legame dei TF.

Il nostro studio precedente ha utilizzato Micro-C per rivelare che la struttura del genoma 3D su scala fine si correla bene con l'attività trascrizionale, formando "punti" o "anelli" (vedere Metodi per la terminologia) nelle intersezioni E-P e P-P9. Nel presente studio, abbiamo identificato oltre 75.000 loop statisticamente significativi nei mESC utilizzando il chiamante di loop di nuova concezione Moustache26 (Fig. 1a) o Chromosight27 (Extended Data Fig. 1a), circa 2,5 volte in più rispetto al nostro precedente rapporto9,26 e circa 4 × più di Hi-C26,28 (Dati estesi Fig. 1b). Attraverso l'analisi dello stato della cromatina locale sugli ancoraggi del loop (dati estesi Fig. 1c,d), abbiamo sottoclassificato questi loop in loop di coesione (~ 13.735), loop E – P (~ 20.369), loop P – P (~ 7.433) e polycomb -contatti associati (~ 700) (Fig. 1a, b), con una dimensione media di ~ 160 kb per i loop di coesione e ~ 100 kb per i loop E – P / P – P (dati estesi Fig. 1e).

75,190 chromatin dots/loops, subclassified into four primary types (Mustache loop caller26; see Methods and Supplementary Note). b, Probability distribution of loop strength for cohesin, E–P, P–P and random loops. Chromatin loop numbers are shown on the left. The box plot indicates the quartiles for the loop strength score distribution (min. = lower end of line, Q1 = lower bound of box, Q2 = line in box, Q3 = higher bound of box and max. = higher end of line). Genome-wide averaged contact signals (aggregate peak analysis (APA)) are plotted on the right. The contact map was normalized by matrix balancing and distance (Obs/Exp), with positive enrichment in red and negative signal in blue, shown as the diverging color map with the gradient of normalized contact enrichment in log10. The ratio of contact enrichment for the center pixels is annotated within each plot. This color scheme and normalization method are used for normalized matrices throughout the manuscript unless otherwise mentioned. Loop anchors are annotated as ‘C’ for CTCF/cohesin, ‘P’ for promoter and ‘E’ for enhancer. Asterisks denote a P < 10−16 using two-sided Wilcoxon’s signed-rank test. The data are presented in the same format and color scheme throughout the manuscript unless otherwise indicated (n = 37 biological replicates)9. c, Genome-wide averaged transcript counts for nascent transcript profiling. Genes are grouped into high, medium and low expression levels based on nascent RNA-seq data (gene body) and rescaled to the same length from TSS (transcription start site) to poly(adenylation) cleavage site (PAS) or TES (transcription end site) on the x axis. d, Rank-ordered distribution of loop strength against gene expression for cohesin, E–P and P–P loops. Gene expression levels for the corresponding chromatin loop were calculated by averaging the genes with TSSs located ±5 kb around the loop anchors. Loop strength was obtained from the same analysis shown in b. The distribution for each loop type was fitted and smoothed by LOESS (locally estimated scatterplot smoothing) regression. Error bands indicate fitted curve ± s.e.m. with 95% confidence interval (CI). e, APAs are plotted by paired E–P/P–P loops and sorted by the level of nascent transcription into high, mid and low levels./p>90% of CTCF peaks and 60% of cohesin peaks are significantly decreased on loss of CTCF (Padj < 0.05; Fig. 3e and Extended Data Fig. 3g). Despite the substantial loss of cohesin peaks, biochemical fractionation experiments show that the fraction of RAD21 associated with chromatin remains fairly constant 3 h after CTCF degradation (Extended Data Fig. 2f, green box). Thus, our results are in line with the widely accepted conclusion that CTCF positions cohesin43. On the other hand, loss of cohesin affects a subset of CTCF binding (Fig. 3c,d)13, resulting in ~20% reduction in the number of CTCF peaks (Fig. 3e) and a slight decrease in its global chromatin association (Extended Data Fig. 2f, blue box)./p> 0.1 µm2 s−1), which can be separated further into slow (Dslow ~0.1–2 µm2 s−1) and fast moving (Dfast > 2 µm2 s−1). Scale bar, 1 μm. f, Aggregate likelihood of diffusive YY1 molecules. Top, bar graph showing fractions of YY1 binned into bound, slow- and fast-diffusing subpopulations. Bottom, YY1 diffusion coefficient estimation by regular Brownian motion with marginalized localization errors. g, Western blots of cytoplasmic (Cyt) and nuclear proteins dissociating from chromatin at increasing salt concentrations (Extended Data Fig. 2b). A subpopulation (~30%) of YY1 stays on chromatin, resisting 1 M washes. Ins, insoluble pellet after sonication; Son, sonicated, solubilized chromatin. Percentage of total shows the signal intensity of the indicated fractions divided by the total signal intensity. Anti-histone 2B controls for chromatin integrity during fractionation. h, FRAP analysis of YY1 bleached with a square spot. Error bars are fitted curve ± s.e.m. with 95% CI. i, Slow-SPT measuring YY1 residence time. Individual molecules were tracked at 100-ms exposure time to blur fast-moving molecules into the background and capture stable binding. The unbinding rate is obtained by fitting a model to the molecules’ survival curve. Each datapoint indicates the unbinding rate of YY1 molecules in a single cell. The box plot shows quartiles of data. Error bars are mean ± s.d. j. Slow-SPT measures YY1’s residence time at multiple exposure times./p>90% depletion after 3 h of IAA treatment (Fig. 7a and Extended Data Fig. 9a). Despite the high degradation efficiency, neither YY1’s nuclear distribution nor its clustering was strongly affected after acute loss of CTCF and cohesin in either live or fixed cells (Fig. 7b,c and Extended Data Fig. 9b). This suggests that the maintenance of YY1 hubs is independent of CTCF and cohesin./p>82% of these loci were associated with promoter regions (Fig. 7f and Extended Data Fig. 9d,e). In contrast, both CTCF and WAPL depletion had a negligible effect on YY1 occupancy (Fig. 7f and Extended Data Fig. 9d,e). In biochemical fractionation analysis, we also observed a similar, though less pronounced, reduction in YY1 chromatin association after RAD21 depletion (Extended Data Fig. 9f). To test whether cohesin facilitates the target search of TFs in general, we performed spaSPT on additional TFs. We thus generated RAD21–AID cell lines stably expressing either HaloTag-conjugated SOX2 or KLF4 and found that the bound fraction of both TFs was reduced by ~20% after 3-h cohesin degradation (Extended Data Fig. 9g). These results suggest that cohesin probably facilitates chromatin binding of TFs in general./p>20% of E–P/P–P loops can cross TAD boundaries and retain high contact probability and transcriptional activity (Fig. 2)18,35; (2) only a very small handful of genes showed altered expression levels after CTCF, cohesin or WAPL depletion (Fig. 3)12,13,14,15,16; (3) CTCF and cohesin loops are both rare (~5% of the time) and dynamic (median lifetime ~10–30 min)34; (4) most of the E–P/P–P loops persist after depletion of these structural proteins (Fig. 4)39,63; (5) CTCF/cohesin generally does not colocalize with transcription loci67; and (6) E–P loops and transcription can be established before CTCF/cohesin interactions on mitotic exit71, in some cases even with no CTCF/cohesin expression36,65,66. Second, YY1 was proposed to be a master structural regulator of E–P interactions25 (Fig. 8, Model 2). However, our Micro-C data are inconsistent with this model, because acute YY1 depletion has little effect on E–P/P–P interactions or gene expression. It is still possible that YY1 specifically connects development-related chromatin loops during neural lineage commitment47, but is less important in the pluripotent state. In summary, we conclude that, in mESCs, CTCF, cohesin, WAPL or YY1 is not generally required for the short-term maintenance of most E–P interactions and the subsequent expression of most genes after acute depletion and loss of function./p>2. Full lists of DEGs are available in Supplementary Table 11./p>2). Full lists of DEGs are available in Supplementary Table 12./p> 100 & intensity > 100 & sigma < 220 & uncertainty_xy < 50; (2) merge: Max distance = 10 & Max frame off = 1 & Max frames = 0; and (3) remove duplicates enabled. This setting combines the blinking molecules into one and removes the multiple localizations in a frame./p>

20 kb). b. Micro-C reproducibility tests. Top: pairwise similarity scores measured by GenomeDisco between UT vs. IAA and UT vs. UT samples using 10-kb resolution of Micro-C matrices. Bottom: similarity scores measured by QuASAR between replicates (light lines) or comparing the UT and IAA-treated samples (dark lines) using Micro-C matrices at 250-kb, 50-kb, 25-kb, and 10-kb resolutions. c. Genome-wide contact decaying P(s) analysis (bottom) and slope distributions of the P(s) curves (top) for UT cells. d. Micro-C contact maps at specific regions or at genome-wide scale across multiple resolutions in the UT and IAA-treated cells. Left to right: examples of Pearson’s correlation matrices showing plaid-like chromosome compartments; saddle plots showing overall compartment strength (A-A: bottom-right; B-B: top left); differential saddle plots showing changes in compartment strength; contact matrices showing TADs along the diagonal; ADA showing all TADs; differential ADA showing TAD strength changes. e. Slope distribution of P(s) curves for UT and IAA-treated cells. Dashed lines highlight the range of genome distances affected by CTCF, RAD21, or WAPL depletion. CTCF depletion had minimal impact on overall interactions across the genome. RAD21 depletion reduced contact frequencies in the range of 10–200 kb but increased interactions at 300 kb – 5 Mb. WAPL depletion showed the opposite trend, with increased contacts at 70–700 kb but reduced contacts at 1–5 Mb. f. Scatter plot of cohesin loops scores in UT and IAA-treated cells. The overlaid heatmap indicates dot density (red: highest, blue: lowest). Dashed lines along the diagonal delimit unchanged loops. g. Loop numbers called by Mustache for UT and IAA-treated cells. The additional loops (n = 5764) identified after WAPL depletion show longer lengths, with a 570-kb median. h. APA for loops across multiple ranges of genomic distance in UT and IAA-treated cells./p> 10), suggesting that while CTCF and cohesin are required for the transcriptional maintenance of only a small subset of genes, those genes tend to require the presence of both factors. Statistical test: Fisher’s exact test. g. Snapshots of Micro-C maps comparing chromatin interactions in the UT (top-right) and IAA-treated (bottom-left) cells surrounding Klf4 locus. Contact maps are annotated with gene boxes and 1D chromatin tracks showing the ChIP-seq signal enrichment in the same region./p>20 kb) interactions. j. Genome-wide contact decaying P(s) analysis (bottom) and slope distributions of the P(s) curves (top) for UT cells. k. MA plot of total RNA-seq and nascent RNA-seq for YY1 degron 3 to 24 hours after IAA treatment. l. Scatter plots of loop scores (quantified using 2-kb-resolution Micro-C data) plotted for E-P or P-P loops in UT and IAA-treated cells. APA for YY1, E-P, or P-P anchored loops plotted for the ΔYY1 degron cell line in UT and IAA-treated cells. m. Micro-C maps comparing chromatin interactions in UT and IAA-treated ΔYY1 cells surrounding Nes gene./p>