dataesr/scientific-paragraphs-categorization huggingface dataset:
For ENCODE, these proportions are 36% and 27% in PCHi-C data and in simulations, respectively (chi-squared test, p-value <10 -10 ). The overlap with enhancers decreases when the distance between contacting regions increases, for both PCHi-C and simulated data (Fig. 2B ). The number of cell types in which interactions are observed is also positively correlated with the presence of enhancers (Fig. 2C ). Similar results were obtained for both species and for all four enhancer datasets (Supplemental Text). Hereafter, we consider that promoters and enhancers are in contact if the corresponding baited fragments of the promoters are in contact with restriction fragments that overlap with the corresponding enhancers, for both PCHi-C and simulated data (Methods). We note that the numbers of enhancers assigned to genes based on PCHi-C data are considerably higher than those obtained with a classical genomic proximity approach where enhancers are assigned to the neighboring genes within a 25 kb -2 Mb distance interval (Methods).
For example, for the human ENCODE dataset, we find that 323,995 gene-enhancer pairs are in contact in PCHi-C data, while the genomic proximity approach predicts 224,446 pairs, for the 9,395 genes with enhancers assigned to them by both methods. The median gene contacts 25 enhancers, but has only 13 neighbor enhancers (Supplemental Fig. S6 ). Moreover, the PCHi-C data predicts regulatory interactions at larger genomic distances (median 277 kb for human ENCODE) than those predicted with the genomic proximity approach (median 88 kb). Only 42,095 gene-enhancer regulatory pairs were predicted by both approaches; the proportion of gene-enhancer pairs in common increases with the genomic distance between the two (Supplemental Fig. S6 ). As a general rule, we can thus confirm that contacts do not generally form between immediately neighboring promoters and enhancers  (Smemo et al.
, 2014). To further test the presence of genuine regulatory interactions in the PCHi-C data, we evaluated the correlations between gene expression and enhancer activity across samples (Methods). Due to the complexity of gene regulatory mechanisms, as well as due to inherent noise in activity measurements, these correlations are expected to be weak  (Hait et al. , 2018). Nevertheless, the activity levels of genes and enhancers connected in PCHi-C data are significantly better correlated than in simulated data (mean Spearman's correlation coefficient 0. 08 in the PCHi-C dataset and 0. 04 in the simulations, Wilcoxon rank sum test p-value <10 -10 ). The correlations between promoter and enhancer activity levels decrease when the distance between the two elements increases (Fig. 2D ). This occurs for both PCHi-C and simulated data, although correlations always remain higher for the PCHi-C dataset.
Our analyses reveal that restriction fragments contacted by baits in PCHi-C data are significantly more conserved than those included in simulated data (Fig. 3B , Supplemental Fig. S7 , Supplemental Text). For the comparison between human and mouse, the median aligned length fraction of contacted fragments is 26. 57% in PCHi-C data (median over N=263,176 fragments), which is significantly higher than the 23. 25% observed in the simulated dataset (median over N=563,651 fragments) (Wilcoxon rank sum test, false discovery rate (FDR) <10 -10 ). This observation holds for all pairwise comparisons between vertebrates, although conservation scores are expectedly weak for comparisons between divergent species (Fig. 3B , Supplemental Text). In contrast, enhancers contacted by promoters in the PCHi-C dataset (N=170,306) are not significantly more conserved than enhancers included in the simulated dataset (N=292,599) (e.
g. , median aligned fraction 63. 21% for PCHi-C data, 63. 16% for simulated data, for human and mouse, Wilcoxon rank sum test, FDR 0. 051, Fig. 3C , Supplemental Text). This suggests that the higher evolutionary conservation of PCHi-C contacted fragments compared to simulated data may be explained by overlap with more enhancers, but not by overlap with better conserved enhancers. We note that enhancers that overlap with restriction fragments in the simulated dataset but not in the PCHi-C data may be missing from the latter because they regulate other genes or function in other cell types. The extent of sequence conservation tends to increase with the distance from gene promoters, for contacted restriction fragments as well as for enhancers (Supplemental Fig. S7 , Supplemental Text). The distance between promoters and contacted regions or enhancers also co-varies with other factors that correlate with sequence conservation, such as the GC content, the overlap with repeats and the gene density in the neighboring regions (Supplemental Fig.
S8 ). Regions included in the PCHi-C data and in the simulated data also often differ with respect to these genomic characteristics. In particular, restriction fragments contacted in PCHi-C data have lower proportions of repetitive sequences than those present in the simulated data (Supplemental Fig. S8 ). This observation holds true when separating regions in classes of similar theoretical mappability (Supplemental Fig. S9 ). We thus wanted to verify whether these genomic correlates could explain the sequence conservation patterns observed in PCHi-C and in simulated data. We first evaluated sequence conservation scores separately for repetitive and non-repetitive positions (Methods). For both classes of positions, conservation levels are higher for restriction fragments involved in PCHi-C contacts than for restriction fragments included in the simulations (Fig.
3F, G , Supplemental Fig. S10 ). We also observed higher sequence conservation in observed versus simulated PCHi-C data when we divided restriction fragments into classes of similar GC content (Supplemental Fig. S12 ), to account for the strong correlation between sequence composition and the rate of evolutionary divergence  (Duret and Arndt, 2008). These results illustrate the complexity of the factors affecting the evolution of cis-regulatory elements. The proportion of repeated elements, gene density, GC content and the distance to the baits are all highly correlated with measures of sequence conservation (Supplemental Table  S2 ). The higher fraction of overlap with enhancers observed in PCHi-C data compared to simulated data may explain part of the difference in sequence conservation.
We restricted this analysis to promoter-enhancer pairs that are separated by distances between 100 kb and 1. 5 Mb in the reference species (Methods). With this convention, synteny "breaks" are evolutionary events that add at least 500 kb to the distance between promoters and enhancers. We found that pairs of contacting promoters-enhancers are maintained in synteny significantly more often than in the simulated dataset (Fig. 4A , Supplemental Text). For example, 95. 9% of pairs between promoters and ENCODE enhancers (N=207,144) are maintained in synteny between human and mouse, compared to 94. 7% in the simulated dataset (N=496,622) (Fig. 4A , chi-squared test FDR < 10 -10 ). At larger evolutionary distances, there is less difference between the PCHi-C data and the simulated interactions (Fig.
6%) than in the simulated data (median=0. 99%, Wilcoxon rank sum test p-value <10 -10 ; Fig. 5B ). The extent of contact conservation is high for comparable cell types (embryonic stem cells or epiblast-derived stem cells, pre-adipocytes and B-lymphocytes), but higher values could be observed in comparisons involving different cell types (Fig. 5A ). This could be explained by technical artifacts leading to better detection sensitivity in some samples, despite our subsampling procedure. Our chromatin contact conservation measures are necessarily under-estimates, given that human and mouse PCHi-C datasets do not consist of the same cell types and that cell-type specific interactions may thus appear as species-specific. Consistent with this, the extent of contact conservation is higher for interactions observed in multiple cell types (Fig.
The complexity of cis-acting regulatory landscapes is associated with gene expression characteristics and with the rates of gene expression evolution To better understand the functional relevance and the phenotypic implications of the promoter-enhancer interactions predicted with PCHi-C data, we examined their relationship with gene expression evolution. We evaluated gene expression patterns using a comparative transcriptome collection spanning several organs and developmental stages  (Cardoso-Moreira et al. , 2019). This dataset allowed us to identify changes in expression profiles, such as changes in organ or developmental stage "preference", the gain or loss of an expression domain, etc. We measured the extent of expression conservation through Spearman's correlation coefficient between relative expression values, for each pair of orthologous genes (Methods).
We also evaluated the conservation of gene expression levels using transcriptome sequencing data for the cell types sampled in both human and mouse PCHi-C datasets (Methods, Supplemental Table  S8 ). We confirm that there is a strong positive correlation between the number of contacted enhancers and gene expression levels in each cell type (Kruskal-Wallis test, p-value < 10 -10 , Supplemental Fig. S19 ). However, as for the conservation of gene expression profiles, we do not find a significant association between the number of contacted enhancers and the conservation of gene expression levels after correcting for the effect of the gene expression level within species (Kruskal-Wallis test, p-value > 0. 05 for all comparisons, Supplemental Fig. S19 ).
We found significant associations between the conservation of regulatory landscapes, on one hand, and gene expression patterns and gene expression conservation, on the other hand, for both distance classes (Supplemental Fig. S21 ). However, the effect is generally weaker for long-range interactions, suggesting that distal regulatory elements contribute less to gene expression evolution than proximal elements (Supplemental Fig. S21 ). We also found significant associations between the degree of sequence conservation of neighbor enhancers and the extent of gene expression conservation (Kruskal-Wallis test, p-value = 2. 1 x 10 -6 for human, p-value = 1. 4 x 10 -10 for mouse, Supplemental Fig. 22 ). However, these associations are less strong than the ones observed with the PCHi-C data (above) and the presence of synteny breaks does not correlate significantly with gene expression conservation with the neighbor enhancer dataset (Supplemental Fig.
We were able to assess the evolution of cis-regulatory landscapes at multiple levels. Starting with primary sequence analyses, we show that regions contacted by promoters in PCHi-C data are better conserved than in simulated data, even after correcting for confounding factors such as repetitive sequence content, GC content etc. Moreover, distant regulatory elements have higher overall levels of sequence conservation than those found in the immediate vicinity of their putative target genes, largely due to a lower overlap with repetitive sequences. Enforcing the idea that long-range contacts between promoters and enhancers are an important mode of gene expression regulation, we showed that synteny breaks that would prohibit these interactions are under-represented, and thus potentially counter-selected.
These observations raise the question of whether expression robustness is achieved not only through the number of regulatory elements that are available to genes, but also through the evolution of a specific genomic architecture. For example, the presence of large intergenic regions around developmental genes may contribute to the "resilience" of their expression patterns during evolution, by preventing unwanted transcriptional or regulatory interference  (Montavon et al. , 2011). With PCHi-C data, we do not observe a strong association between the numbers of contacted enhancers and gene expression conservation, beyond what is explained by expression levels and expression breadth. This puzzling observation might be explained by the fact that regulatory relationships inferred with PCHi-C data are to a great extent orthogonal to genomic architecture: enhancers contacted by promoters are not necessarily their immediate neighbors.
Another explanation may reside in the discrepancy between the PCHi-C, gene expression and enhancer datasets that we used here, in terms of biological sampling. Unlike previous studies, which analyzed genes and enhancers that are active in the same tissue  (Berthelot et al. , 2018) , here we rely on heterogeneous sample collections for the different types of data. Despite this drawback, we could uncover significant associations between the rate of regulatory landscape evolution and the pattern of gene expression evolution, by analyzing relative expression profiles across comparable organs and developmental stages  (Cardoso-Moreira et al. , 2019)  biological samples directly related to organ development, there is evidence that our PCHi-C dataset provides a good starting point to study its underlying cis-regulatory landscapes.
For example, we found that genes that have evolutionarily conserved chromatin contacts at large genomic distances are enriched in functional categories associated with embryonic development. Analyzing expression profiles with this comparable transcriptome collection has the advantage of reducing technical biases linked to gene expression comparisons between distant species, as well as of providing a broader overview of the pattern of expression evolution. Despite the functional redundancy of cis-regulatory networks, by jointly analyzing numerous biological conditions we increase the likelihood of observing the molecular consequences of enhancer evolution. Nevertheless, performing similar analyses with comparable PCHi-C and transcriptome sequencing data would likely reveal even stronger relationships between regulatory landscape evolution and gene expression evolution.
Methods Promoter Capture Hi-C data processing We collected and processed publicly available Promoter Capture Hi-C (PCHi-C) data for human  (Choy et al. , 2018; Freire-Pritchett et al. , 2017; Javierre et al. , 2016; Mifsud et al. , 2015; Pan et al. , 2018; Rubin et al. , 2017)  and mouse samples  (Comoglio et al. , 2018; Koohy et al. , 2018; Novo et al. , 2018; Schoenfelder et al. , 2015 Schoenfelder et al. , , 2018;; Siersbaek et al. , 2017). We selected PCHi-C datasets that were generated with experimental procedures similar to those described by Schoenfelder and co-authors  (Schoenfelder et al. , 2015). Genome fragmentation was generated with the HindIII restriction enzyme in all cases, ensuring identical restriction maps across all samples within a species. We processed PCHi-C data for 26 samples and 16 cell types for human and 14 samples and 8 cell types for mouse (Supplemental Table  S1 ).
The data include several cell types (embryonic stem cells, epiblast-derived stem cells, adipocytes and B cells) that are comparable across species, although cell culture procedures and differentiation stages may differ (Supplemental Table  S1 ). To homogenize computational analyses across datasets and species, we re-processed all PCHi-C raw data. We used the HiCUP pipeline  (Wingett et al. , 2015) , which aligns reads, filters artifactual fragments (such as circularized reads and re-ligations), and removes duplicates. We mapped PCHi-C reads to the human hg38 (GRCh38. p12) and mouse mm10 (GRCm38. p6) genome assemblies, downloaded from Ensembl release 94  (Cunningham et al. , 2019) , using Bowtie version 2. 3. 4. 3  (Langmead and Salzberg, 2012). We called interactions with the CHiCAGO pipeline  (Cairns et al.
, 2016). We selected chromatin contacts with a CHiCAGO score greater or equal to 5 in at least one sample (Supplemental Table  S1 ). An interaction between a bait and a restriction fragment is said to be detected in a given cell type if it was detected in at least one of the corresponding samples. We combined detected interactions across samples and found 910,180 unique interactions between 19,389 baited restriction fragments and 308,359 other fragments for human, and 824,406 interactions between 21,858 baited fragments and 247,668 other fragments for mouse. These data are provided in Supplemental Dataset S1. We further focused on intra-chromosomal contacts (cis-interactions) separated by a genomic distance of 25 kb to 2 Mb, computed between the midpoint of baited and contacted regions. We thus exclude short-range interactions that have high levels of background noise  (Cairns et al.
, 2016) , as well as interactions that are beyond the typical size observed for topologically-associating domains  (Dixon et al. , 2012). We discarded interactions that involved restriction fragments smaller than 150 bp or larger than 50 kb. We also removed interactions probably involved in structural variation or potential genome assembly issues, i. e. pairs of contacting regions separated by a large distance in a reference species (>1Mb), and by a small distance (<100kb) in the target species. The list of excluded restriction fragments is provided in Supplemental Dataset S1. Finally, we restricted our analyses to contacts between baited and non-baited restriction fragments.
We then randomly drew contacts among the list of all possible interactions based on this empirical probability distribution. We respected the number of interactions observed in the real PCHi-C data, for each bait. However, we discarded a posteriori those simulated interactions that fell within a baited restriction fragment; after this filtering step, the number of contacts per bait are lower in the simulated data than in the PCHi-C data (Supplemental Fig. 1 ). The simulated interaction data are available in Supplemental Dataset S2.
Subsampled chromatin interaction dataset To minimize differences in detection sensitivity among PCHi-C samples (Supplemental Table  S1 , Supplemental Text), we generated subsampled datasets. We first computed the minimum number of observed interactions (N) across all samples for each species (79,843 for human and 70,475 for mouse). We then ranked interactions based on their CHiCAGO score and kept the strongest N interactions from each PCHi-C sample, reasoning that the relative ranking would remain unchanged if detection power were reduced. For simulated datasets we randomly re-sampled N interactions. We applied the same filtering steps described previously (e. g. , discarding bait-bait interactions, trans interactions and interactions occurring at distances < 25 kb or > 2 Mb) on the subsampled data before analyzing them further.
We used these subsampled interaction datasets to evaluate sample clustering within species (Supplemental Fig. S3 , Supplemental Text), and for pairwise comparisons of contact conservation between human and mouse samples (Fig. 5A, B , Supplemental Text). As PCHi-C and simulated datasets differ in terms of total numbers of interactions after pooling all available samples (Supplemental Text), we randomly subsampled the pooled simulated dataset to obtain the same number of interactions as in pooled PCHi-C data. These datasets were used in the contact conservation analyses that rely on pooled samples across species (Fig. 5C ,D, Supplemental Text) and are available in Supplemental Dataset S1 and Supplemental Dataset S2.
Prediction of contacts between gene and enhancers We constructed gene-enhancer pairs by associating to each "baited" protein-coding gene all the predicted enhancers that overlapped with the restriction fragments contacted by its baits. We applied this procedure to each enhancer dataset described above, obtaining four different contact datasets for human and two datasets for mouse (Supplemental Dataset S4). CC-BY-NC 4. 0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted December 13, 2021. ; https://doi. org /10. 1101/2021. 02. 26. 432473  doi: bioRxiv preprint non-conserved and were given a conservation score of 0.
The predicted homologous regions were then aligned with Pecan  (Paten et al. , 2008). We computed the conservation score as the ratio between the total number of aligned (without gaps) base pairs and the total number of positions in the alignment. To better evaluate the determinants of sequence conservation patterns in PCHi-C data, we also measured sequence conservation separately for repetitive and non-repetitive sequences, using the information provided in the repeat-masked genome sequence available in Ensembl  (Zerbino et al. , 2018). We extracted exonic coordinates from the Ensembl database and we masked exons before evaluating sequence conservation. We discarded sequences whose alignment length was smaller than 10 bp. We applied the same alignment procedure to predicted enhancers. We also analyzed the phyloP basewise conservation score  (Pollard et al.
, 2010). We retrieved from the UCSC Genome Browser  (Hinrichs, 2006)  phyloP scores calculated from multiple alignments of 30 vertebrate species for human and for 60 vertebrate species for mouse. We computed average phyloP scores for each restriction fragment and enhancer, across all non-exonic bases that had phyloP coverage. We also computed average phyloP scores separately for repetitive and non-repetitive sequences, using RepeatMasker annotations downloaded from the UCSC Genome Browser  (Hinrichs, 2006). The alignment statistics and phyloP scores are available in Supplemental Dataset S6 and in Supplemental Dataset S7.
Synteny conservation We defined synteny conservation between a reference and a target species as the presence of the gene and of the predicted enhancer on the same chromosome and at a distance of less than 2 Mb in both species. We restricted this analysis to protein-coding genes with 1-to-1 orthologues in the target species, as predicted in the Ensembl database (release 99 for macaque, release 94 for all other species). We also selected only enhancers that had predicted homologous regions in the target genome, as defined above. For each pair of species, we discarded the 10% least conserved enhancers based on the alignment score defined above. We further restricted this analysis to promoter-enhancer pairs that are separated by distances between 100 kb and 1. 5 Mb in the reference species. We then asked whether the predicted homolog of the contacted enhancer was on the same chromosome and within 2 Mb of the TSS of the orthologous gene.
The proportion of conserved contacts was computed with respect to the number of pairs satisfying all previous criteria (i. e. , homology prediction for the baited gene and the contacted enhancer, presence of baits for the orthologous gene in the target species, distance and unbaited contacted fragment filters). We evaluated the contact conservation for each pair of human and mouse samples. We performed this analysis on the downsampled dataset, which comprises the same number of contacts for each sample. We also evaluated the extent of chromatin contact conservation for the pooled PCHi-C dataset. In this case, we used as a comparison the subsampled pooled simulated dataset, which matches the number of interactions observed in the pooled PCHi-C dataset. For the analyses that contrast the extent of regulatory landscape conservation and gene expression conservation, we use the entire PCHi-C data, without subsampling.