dataesr/scientific-paragraphs-categorization huggingface dataset:
Role of SOX9 in RNA processing in foetal testis SOX proteins could have a role in splicing regulation  (71)  and a direct link between SOX9 and the RNA processing of Col2a1 (one of the SOX9 targets identified in our bovine ChIP-seq experiment) was demonstrated in chondrocytes  (72). Therefore, we used our RNA-seq dataset to investigate SOX9 role in splicing of its target genes in foetal testis. Splicing of 154 mRNAs, of which 70 (45%) were bound by SOX9, was quantitatively different in Sox9 / XY and WT XY testes (Figure  3A ). All five classes of known alternative splicing events  (73)  were affected by SOX9 absence: exon cassette (also known as exon skipping), intron retention, alternative 3 acceptor site, alternative 5 donor site and mutually exclusive exons. Quantitative analysis of the WT XY and Sox9 / XY RNA-seq datasets allowed calculating the Percent-Spliced-In (PSI) index that indicates the splicing efficiency for transcripts and can range from +1 (totally include) to -1 (totally exclude).
The PSI values for transcripts of genes bound by SOX9 indicated that splicing efficiency differed between the WT XY and Sox9 / XY datasets (Figure  3A ; for details see Supplementary Table  S3 : Sheet 'XY WT versus XY KO'). Specifically, SOX9 absence affected the splicing of genes with important roles in sex determination and differentiation, such as Atrx (74), Fgfr2 (75) and Gadd45g  (52). Importantly, in 20 of the 70 differentially spliced genes bound by SOX9 (28%), SOX9 peaks overlapped with the exon/intron boundary involved in the splicing events (gene names underlined in Figure  3A , and examples in Figure  3B ). Analysis of somatic and germ cellspecific transcriptomic datasets from foetal male and female mouse gonads  (64)  showed that the expression of SOX9bound genes with alternatively spliced transcripts was not particularly sex-or cell-specific (not shown).
This suggests that SOX9 may regulate some discrete molecular mechanisms that require finer transcriptomic analyses to be revealed. We next investigated SOX9 mediated sex-specific splicing by searching for common splicing events between WT XY and Sox9 / XY and between WT XY and WT XX datasets. Compared with WT XY gonads, several transcripts showed differential splicing events that involved the same exons in Sox9 / XY and WT XX gonads (Figure  3C ). Their respective PSI values were always in a similar range when comparing WT XY with Sox9 / XY or WT XX gonads (Supplementary Table  S3 : Sheets 'XY WT versus XY KO' and 'XY WT versus XX WT'). This indicates that splicing of these exons is similar in Sox9 / XY and WT XX gonads. We conclude that SOX9 regulates malespecific splicing of several of its target genes.
In all cases, these splicing events will modify the resulting protein sequences. Moreover, intron retention can also modify the intrinsic level of the translated proteins. For instance, in XX WT and Sox9 / XY, retention of the second intron of the Ubfd1 gene leads to the disruption of the open reading frame and to a shorter truncated protein of 204 amino acids instead of full length 368 amino acids UBFD1 (Figure  3B , panel Ubfd1). Moreover, at the transcriptional level, published transcriptomic datasets  (64)  and our RNAseq data showed no variation in Ubfd1 gene expression between male and female gonads, suggesting that SOX9 could control the protein level of active UBFD1 via a discrete splicing mechanism that does not affect the transcript level. Similarly, the second intron of Gadd45g, which has a role in sex-determination  (52, 76) , was retained in Sox9 / XY testes, leading to a shorter truncated protein of 52 amino acids instead of full length GADD45g (159 amino acids in length).
The PSI values reported here correspond to the PSI value difference between genotypes. Underlined names represent genes where SOX9bound peaks overlapped with the exons/introns involved in the splicing event; their total number is indicated between brackets in the relevant pie-chart. (B) Sashimi plots showing examples of the splicing events altered in Sox9 / testes and described in Figure  3A , obtained from RNA-seq datasets. These transcripts correspond to genes that are bound by SOX9 in mouse or mouse and cattle, with ChIP-seq peaks in the vicinity of regions of splicing events (genes underlined in Figure  3A ). Red bars, position of SOX9 peaks; blue arrows, direction of transcription. For each genotype (WT XY, KO XY and WT XX gonads), the quantification of the PSI value ( ) with 95% confidence intervals (histograms on the on right side of each panel) obtained using MISO are reported.
Therefore, we developed a normalization bioinformatics method to detect sequences enriched in known consensus DNA motifs  (41, 43, (77) (78) (79)  recognized by the five Sertoli cell differentiation factors in the mouse and bovine SOX9 ChIP-seq datasets. The motif enrichment fold was the ratio of the number of binding motifs found in the ChIP-seq datasets versus the number found in the corresponding scrambled sequences (30 sets). The mouse and bovine ChIP-seq datasets were similarly enriched in DNAbinding motifs for SOX9, DMRT1 and GATA4 (i. e. the ratio ChIP/scrambled was >1) (Figure  4A , red and orange bars, left histogram) compared with unrelated genomic regions (blue bars). Conversely, SF1 motifs were not enriched and WT1 motifs were non-specifically enriched in the ChIP-seq datasets as well as in the unrelated genomic regions.
As a control, binding motifs of transcription factors not related to Sertoli cell differentiation were not enriched in the two ChIP-seq datasets (Figure  4A , right histogram). SOX9/DMRT1/GATA4 binding motif enrichment was neither due to repetitive sequences (Supplementary Figure  S3A ) nor dependent on the position of the peaks relative to the gene body (Supplementary Figure  S3B ). Thus, SOX9 target sequences are enriched in binding motifs for SOX9, GATA4 and DMRT1. The same motif enrichment was also found in the orthologous regions of twelve other mammal genomes (Supplementary Figure  S3C ), suggesting that this genomic feature is conserved among mammals. To determine whether this global motif enrichment reflected a situation existing at each genomic fragment bound by SOX9, we investigated the co-occurrence of SOX9, GATA4, DMRT1, SF1 and WT1 binding motifs in both SOX9 ChIP-seq datasets.
The number of occurrences of DNA motifs for the five Sertoli cell differentiation/reprogramming factors was plotted against each other to assess whether a linear correlation existed between motifs (examples of scatterplots with Pearson correlation coefficients, PCC, are presented in Supplementary Figure  S3D ). The PCC obtained for the ChIP-seq datasets and control scrambled sequences showed a strong correlation (PCC between 0. 7 and 0. 9) among SOX9, GATA4 and DMRT1 motifs in both mouse and bovine ChIP-seq datasets (Figure  4B ). By contrast, SF1 motifs were poorly represented (Figure  4B  and Supplementary Figure  S3D ; PCC: 0. 30-0. 37) and WT1 motifs showed almost no correlation with the other four motifs. No correlation was observed between SOX9 motifs and TF motifs (negative control) and for any combination with the corresponding scrambled sequences.
The co-occurrence of SOX9, GATA4 and DMRT1 DNA-binding motifs was also observed in the orthologous regions of SOX9 ChIP-seq datasets from the twelve mammalian genomes (Figure  4C ). This indicates that the association of SOX9, GATA4 and DMRT1 DNAbinding motifs in these genomic regions is conserved among mammals. Compilation of the three motifs enriched in both ChIP-seq datasets showed that they were highly similar in rodent and bovine species (Supplementary Figure  S3E ), but displayed a milder selectivity than the consensus motifs ob-tained by in vitro selection for SOX9  (43) , GATA4 (77) and DMRT1  (41).
Characterization of a Sertoli cell signature (SCS) in SOX9 genomic targets in foetal testis To determine whether this SOX9, GATA4 and DMRT1 DNA-binding motif enrichment at SOX9-bound genomic regions was unique to foetal Sertoli cells, we analysed SOX9, GATA4 and DMRT1 binding motif enrichment in randomly selected ChIP-seq datasets for 13 transcription factors and eight SOX factors expressed in various tissue and cell types (http://www. ncbi. nlm. nih. gov/geo/). Compared with the mouse SOX9 ChIP-seq foetal testis dataset, we did not detect any motif enrichment in ChIP-seq datasets for the unrelated transcription factors (Figure  4D ) and other SOX factors (Figure  4E ). Analysis of SOX9 ChIP-seq datasets from seven other mouse tissues highlighted a slight SOX9/DMRT1/GATA4 binding motif enrichment in rib chondrocytes  (80) , atrioventricular canal (AVC), limb  (81)  and hair follicle stem cells (HF-SCs) (82) (Figure  4F , blue bars).
This enrichment was similar to the one observed for other transcription factors and SOX factors. On the other hand, in mouse basal cell carcinoma (BCC)  (83)  and human colorectal cancer cells (84), SOX9/DMRT1/GATA4 binding motif enrichment was comparable to the one observed in foetal testis, but for the DMRT1 motif (Figure  4F , green bars). After conversion of the coordinates of the relevant human regions to the mouse orthologous regions, we found an important peak overlap between our SOX9 ChIP-seq testis datasets and the colorectal cancer cell and BCC datasets, but not with the rib chondrocyte, limb, AVC and HF-SC SOX9 ChIP-seq datasets (Supplementary Figure  S4 ). This shows that, at least in these two cancer types, SOX9 binds to regions with genomic features very similar to those observed in differentiated foetal Sertoli cells.
Binding motifs in the SCS are clustered and organized We then asked whether the SOX9/DMRT1/GATA4 binding motifs were organized within ChIP-seq peaks. In most of the cases, adjacent binding motifs were separated by less than 50 bp both in the mouse and bovine ChIP-seq datasets, indicating that they clustered together (Supplementary Figure  S5A ). By analyzing nucleotide spacing between motifs in both murine and bovine datasets (Supplementary Figure  S5B ) we observed that the distance between adjacent motifs was lower than ten nucleotides and remarkably conserved from mouse to cattle in term of distance and orientation. For instance, SOX9 motifs were preferentially on the same strand and separated by one or three nucleotides. Altogether, our results show that SOX9/DMRT1/GATA4 motifs are strongly clustered in both species.
To confirm this, the distance of each motif relative to the nearby TSS was measured in peaks positioned near the TSS (i. e. from -1 kb to +1 kb around the TSS). The distribution of the three binding motifs analysed together (Figure  4G ) or separately (Supplementary Figure  S5C ) followed a bimodal pattern around the TSS of both mouse and bovine SOX9 target genes, but not of unrelated genes (not bound by SOX9). This shows that in the mouse and cattle, SOX9/DMRT1/GATA4 binding motifs cluster and that for peaks around TSS, SCSs are located preferentially at -500/400 bp or at +600 bp from the TSS. Next, we asked whether the three binding motifs showed a preferential order within SCSs and whether this order was conserved in mouse and bovine orthologous regions bound by SOX9. To this aim, we counted the occurrence of every possible multimer of each binding motif (from trimers to octamers) in both ChIP-seq datasets, taking in account their direct (D) or reverse orientation (R) relative to the direction of transcription of the neighboring gene.
Genes bound by DMRT1 in foetal testis harbour SCSs only if they are also bound by SOX9 In foetal testis, Dmrt1 is expressed in Sertoli and germ cells  (85). As DMRT1 motifs were highly correlated with SOX9 motifs within SCSs, we hypothesized that these two transcription factors have common target genes in foetal Sertoli cells. We first confirmed that both SOX9 and DMRT1 were co-expressed in the nucleus of Sertoli cells at E13. 5 (Figure  5A ). Then, to determine whether SOX9 and DMRT1 had common target genes, we compared E13. 5 mouse DMRT1  ← ------------------------------------------------------------------------------------------- bars) were used as controls. (B) Pearson correlation coefficients (PCC; Y axis) for the indicated combinations of DNA binding motifs in the mouse and bovine ChIP-seq datasets and scrambled sequences.
(C) PCC (Y axis) for the three combinations of DNA binding motifs (SOX9-GATA4; SOX9-DMRT1 and GATA4-DMRT1) in the mouse (red bars), bovine (orange bars) ChIP-seq datasets and in orthologous regions from twelve other mammals (blue bars). Genome builds are detailed in Supplementary Figure  S3C. (D) SOX9, DMRT1 and GATA4 binding motif enrichment in ChIP-seq datasets for transcription factors (GATA1, GLI1, PPARg, MEF2, ZFP281, CUX2, NAC1, REX1, NANOG, GFI1b, ASCL1 and TBX5) expressed in various tissue and cell types. (E) SOX9, DMRT1 and GATA4 binding motif enrichment in ChIP-seq for other SOX factors (SOX2, SOX2, SOX3, SOX4, SOX6, SOX10, SOX11 and SOX17). ES, ES cells; NPC, neural progenitor cells; SchC, Schwann cells; ExEnd, extra-embryonic endoderm; Fet Test, foetal testis. (F) SOX9, DMRT1 and GATA4 binding motif enrichment in SOX9 ChIP-seq datasets from various tissues.
For (D), (E) and (F), see description of tissue/cell types used to obtain the datasets in Supplementary Tables S4 to S6. For panels (A), (D), (E), (F): data are the mean ± SD; ****P < 0. 0001; ***P < 0. 001; **P < 0. 01; ns: not significant (ANOVA with the Geisser-Greenhouse correction for multiple comparisons). (G) Graph showing for peaks close to the TSS in the mouse and bovine SOX9 ChIP-seq datasets, the averaged curves of the localization of the SOX9, GATA4 and DMRT1 binding motifs relative to the TSS. X-axis: nucleotide distance from the TSS. Y-axis: count of the three motifs. (25) and SOX9 ChIP-seq datasets and found that among the 2430 and 6419 respective peaks, 671 overlapped (Figure  5B ). As a control, 30 sets of 2430 regions of equivalent size and randomly selected in the mouse genome displayed very little overlap (<10 regions) with the 6419 peaks of the SOX9 ChIP-seq dataset (not shown).
From these 571 common regions, we also identified 411 (71%) orthologous regions in the bovine SOX9 ChIP-seq dataset (not shown). We then analyzed separately the SCS content of the two populations of peaks in the DMRT1 ChIP-seq dataset (i. e. regions bound or not by SOX9). Overall, regions bound by DMRT1 displayed the same global enrichment for SOX9, DMRT1 and GATA4 binding sites as observed in the SOX9 ChIP-seq dataset. However, in peaks bound only by DMRT1, SOX9 and GATA4 sites were not as enriched as DMRT1 binding sites (Figure  5C ). Similar results were obtained by calculating the PCCs (Supplementary Figure  S7 ). From these analyses, we conclude that regions bound by DMRT1 in foetal testis display SCSs only when they are also bound by SOX9. On the basis of these results, we hypothesized that on Sertoli cells chromatin, DMRT1 and GATA4 could cohabit in proximity of SOX9 on it target genes.
SCS scanning identified TRIM28 as a new SOX9 partner in foetal testis As regions bound by DMRT1 with a SCS were also bound by SOX9 in foetal testes, we hypothesized that by searching for SCSs in ChIP-seq datasets of various unrelated transcription factors we might identify new proteins that contribute to SOX9-mediated transcriptional regulation in Sertoli cells. By analyzing ChIP-seq datasets for 50 different transcription factors obtained from GEO, we identified a SCS in the dataset for the nuclear factor TRIM28 in mouse embryonic stem (ES) cells (TRIM28 ChIP-seq dataset) (86) (Figure  6A ) where 1501 peaks (44. 5% of 3370) overlapped with those of the mouse SOX9 ChIP-seq dataset (Figure  6B ). As this finding indicates that TRIM28 can bind to the same genomic regions as SOX9, the two might physically interact.
To investigate the functional role of SOX9 in Sertoli cells, we then knocked-out the Sox9 gene after primary sex determination specifically in Sertoli cells. This allowed creating a hypomorphic mutant of the SOX E group. Moreover, in this genetic background, Sox8 remains active and plays a partial compensatory role  (67). Therefore, the effects on the E13. 5 gonad transcriptome should result from transcriptional mis-regulation only in Sertoli cells. In Sox9 / XY gonads, 240 genes were down-and 107 genes up-regulated compared with wild type XY gonads. SOX9-bound genes were more frequently found among the downregulated than the upregulated genes (40% versus 22%), suggesting that SOX9 acts mainly as a transcriptional activator. Alternatively, Sox8 could replace Sox9 for this function because in the double Sox9 and Sox8 knock-out, FoxL2, a female gene bound by SOX9 in the mouse and bovine ChIP-seq datasets, shows ectopic testicular expression  (88).
In Sox9 / XY gonads, splicing variations encompassed the five major splicing events. For example, incorporation of exon 2 in the Atrx transcript was more frequently observed in mutant testis and ovary than in wild type testis. Fgfr2, which is crucial for sexdetermination  (75) , has two spliced isoforms that include either exon 8 (Fgfr2b) or exon 9 (Fgfr2c that encodes the high affinity FGF9 receptor)  (89). Our results show that at E13. 5, SOX9 inhibits the inclusion of exon 9 in the Fgfr2 transcript and promotes the production of Fgfr2b, as already described in pancreas  (90)  and in agreement with our previous observation that the Fgfr2c isoform is more expressed in XY sex-reversed ovaries than in XY testes  (91). ------------------------------------------------------------------------------------------ SOX9 ChIP-seq dataset from mouse foetal testes (red bars).
Therefore, instead of comparing the nucleotide conservation of putative binding sites for transcription factors among orthologous regions, we chose to analyse the distribution of pre-defined binding sites for five Sertoli cell differentiation/reprogramming factors (SOX9, GATA4, DMRT1, SF1 and WT1)  (18)  in the whole ChIP-seq datasets from both mammals. This led to the definition of a Sertoli cell signature (SCS) in which SOX9, GATA4 and DMRT1 motif enrichment characterizes the genomic regions bound by SOX9 in Sertoli cells. This genomic signature is conserved, as indicated by its presence in orthologous regions from twelve other mammals. We also observed this signature in other vertebrate genomes, for instance, in the chicken genome (not shown) where DMRT1 plays a crucial role in sex determination  (96).
Analysis of SCS organization revealed a strong motif clustering, where motifs are separated by fewer than 50 nucleotides. Moreover, in peaks close to the gene body, motifs clustered at a precise distance upstream or downstream the TSS. This suggests that this genomic feature has functional significance in transcriptional regulation. Moreover, SOX9 and GATA4 binding motifs were preferentially associated, while the position of DMRT1 motifs was less constrained. Importantly using sequential ChIP-qPCR we showed that DMRT1 and GATA4 co-localize with SOX9 on chromatins of its target genes. Additional analyses are required to determine how these factors collaborate in gene regulation and how they bind to SCSs. As DMRT1 is expressed in Sertoli and germ cells at E13. 5, we searched for potential SCSs in DMRT1 ChIPseq datasets  (25).
We found that peaks bound by both SOX9 and DMRT1 had SCSs, while those only bound by DMRT1 presented a different motif enrichment pattern. This suggests that a genomic region bound by DMRT1 will display a SCS only if it is also bound by SOX9. As a proof of concept, we used our global motifenrichment scanning method to identify new factors with SCSs in ChIP-seq datasets that could have functional relationship with SOX9. We found that in ES cells, TRIM28 interacts with many regions that are also bound by SOX9 in foetal testis and showed that the two proteins form a complex within the foetal testis. Moreover, as TRIM28 colocalizes on SOX9 target genes in vivo and cooperate with SOX9 in vitro to regulate gene expression, our results highlight an important role for TRIM28 in SOX9-dependent transcriptional regulation.
(C) Examples of peak position conservation in the mouse and bovine ChIP-seq datasets. For each gene, the upper panel represents the mouse gene and the lower panel the bovine gene. The input background was subtracted using IGV. Binding peaks called by MACS are indicated by asterisks. As in the BosTau6 genome assembly the TSS of bovine FGF9 are missing, they were determined using LiftOver with mm9 and added by hand using IGV. Black arrows, direction of transcription; red stars, transcription start sites.
(D) RT-qPCR analysis of SOX9-mediated induction of gene expression in tet-SOX9-NT 2 D 1 cells upon addition of tetracycline (Tc). Results are shown as the mean ± SD (n = 3); ****P < 0. 0001; ***P < 0. 001; **P < 0. 01 compared with cells without tetracycline (ANOVA with the Geisser-Greenhouse correction for multiple comparisons). (E) Pie chart showing the classification of genes that are upregulated in XY Sox9 Δ/Δ testis versus XY WT in genes bound or not by SOX9 (mouse ChIP-seq dataset). Only 8% of these SOX9-bound genes are down-regulated at E13. 5 in wild type Sertoli (35%) versus granulosa cells (64).
(H) The 20 most represented tetramer motifs among peaks bound by SOX9 in the mouse (left) and bovine (middle) datasets. On the right: the top 20 conserved tetramers among orthologous genes bound by SOX9 in the mouse and bovine ChIP-seq datasets. Motifs are in the direct (D) or reverse (R) orientation relative to the transcription of the nearby gene (black arrow). Black and red lines show identical tetramers in the two mammalian species. (I) Electrophoretic mobility shift assays showing binding of in vitro translated SOX9, DMRT1 and GATA4 proteins to DNA probes derived from mouse ChIP-seq peaks assigned to the Lats2, Lm04 and Mrpl45 genes and containing SCSs. +/ indicate programmed and un-programmed lysates. Arrows indicate protein/DNA complexes. †: SOX9 dimers; *: SOX9 monomers; BS, control probes with the consensus binding motifs for SOX9, DMRT1 and GATA4.
ACKNOWLEDGEMENTS We thank the staff of the  Montpellier Imaging Facility (MRI)  for their help with microscopy experiments. We thank  Morgane Thomas-Chollier  and  Jacques van Helden  for helpful discussion on motif scanning. We thank  Sergei Tevossian  for the mouse GATA4 expression plasmid. We are grateful to  Dominique Giorgi  for critical reading of the manuscript. We thank  Elisabetta Andermarcher  for manuscript editing.
Results and Discussion Crystallization, Structure Determination and Global Structural Features. Initially, we set out the crystallization of seven EcTRX point mutants: L94A, L99A, E101G, F102A, L103A, N106A and L107A. We were unsuccessful in crystallizing L99A, F102A and L103A, the least stable variants, although many attempts were carried out using automated robotics to screen a large number of crystallization conditions. Conformational instability of these variants might be the cause of difficulty in crystallization, as the substituted residues participate in key core interactions. Well-diffracting crystals of variants L94A, E101G, N106A and L107A were obtained, and their structures were determined by molecular replacement at high resolution (PDB: 5HR2, 5HR0, 5HR3 and 5HR1, respectively).
Trial refinements on the most affected dataset indicated that this effect is actually an artifact of data collection, due to partial reduction by radiation damage (see Supplementary Fig. S1 ). Therefore, we modeled all of the cysteine pairs as disulfide bonds. Local Effects and Variability in the Proximity of the Mutation Sites. The N106A variant was designed to eliminate a hydrogen bond linking the side-chain of Asn106 with the carbonyl oxygen of Phe102, thus N106A is expected to destabilize the native structure. In a previous work, we found that all of the native-state signatures of the N106A mutant were essentially identical to wild-type EcTRX  12. The three-dimensional structure of this variant reveals that the hydrogen bond partner of Phe102 is now the main-chain of residue 106, through its amide nitrogen.