dataesr/scientific-paragraphs-categorization huggingface dataset:
01; R 2 = 0. 69, n = 9). These covariations most likely result from kinetic effects as defined by  Mc-Connaughey (1989) , here recorded at the intra-specific level. Our new data replicate the observations made by  Bajnai et al. (2018)  concerning kinetic fractionation in brachiopod shells. In consequence, we follow their conclusions and consider it to be the case that different growth rates of the specimens are responsible for the second-order isotopic variability and the deviation from the stable isotope and clumped isotope equilibrium observed in brachiopod datasets, both at an inter-specific and intra-specific level. Neither minor nor trace elements are incorporated into brachiopod shell calcite in equilibrium with seawater relative to abiogenic calcite  (Rollion-Bard et al. , 2019; Simonet Roda et al.
, 2022). Kinetic effects are known to influence trace element incorporation into calcite  (Lorens, 1981; Busenberg and Plummer, 1985; Tesoriero and Pankow, 1996; Gabitov et al. , 2011 Gabitov et al. , , 2014) )  and may be a process, among others  (Jurikova et al. , 2020) , that explains trace element incorporation into brachiopod shell calcite. Owing to the very limited knowledge of brachiopod shell growth rates (for only 5 out of 17 species in our dataset), we are unable to directly test this hypothesis. However, if kinetic effects have such an influence on element incorporation into brachiopod shell calcite, we should observe strong negative correlations of Sr/Ca, Na/Ca and Li/Ca ratios with 18 O eq. We observe such significant correlations in the new dataset in both the inner-and outerlayer subsets (0.
40 < R 2 < 0. 55, p slope < 0. 01), with a significant slope change between the two subsets. The variation in element / Ca related to a similar variation in 18 O eq , as suggested by linear regression models, is about twice as high in the inner-layer subset than in the outer-layer subset. This difference may be explained by the different pathways brachiopods use to form their different shell layers  (acicular, fibrous and prismatic/columnar; Simonet Roda et al. , 2022). However, while looking at Aerothyris kerguelenensis, which showed the most important kinetic effects according to stable isotope data, there is no significant correlation of element / Ca ratios with 18 O eq. Element / Ca ratios appear to be independent from isotope kinetic effects at the species level despite strong correlations of element / Ca ratios and 18 O eq in the multi-specific dataset.
In line with the significant correlation between 18 O eq and element / Ca values in brachiopod shell inner layers, the environmental and taxonomic distributions of isotopic offsets ( 18 O eq , 47 offset) in our dataset are similar to the distribution of element / Ca values. We observe more deviation from the equilibrium in high-latitude environments than in tropical environments, and we generally observe more deviation from isotopic equilibrium in Terebratellidina than in Terebratulidina in the whole dataset, although the difference is not as striking as that documented for trace elements. As the geographical distribution of the two suborders in the new dataset is highly biased, this trend cannot be confidently interpreted as arising from different shell formation processes in the different groups.
Kinetic effects can be, on first approximation, controlled by shell growth rates  (McConnaughey, 1989; Bajnai et al. , 2018). Therefore, both biological and environmental parameters should control the extent of the kinetic effects. At first order, one can expect species growing larger shells to record more important kinetic effects. Moreover, shell growth rates in calcifying organisms increase when environmental conditions are close to optimal living range and decrease when the conditions strongly derive from that optimum, a pattern that is dependent on taxon-related preferences  (Schöne, 2008). However, the poor knowledge of brachiopod shell growth rate, relative to other marine calcifying organisms, prevents an in-depth appreciation of its exact role. Kinetic effects are still insufficient to describe all of the deviation from equilibrium observed here, but our dataset does not allow for a discussion of other potential factors  (Jurikova et al.
Our new dataset confirms previous evidence for the interpretation of nonequilibrium isotopic fractionation interpreted as significant kinetic effects recorded in the brachiopod shell inner layers. In general, the precipitation of brachiopod shells that takes place out of isotopic equilibrium leads to (1) an underestimation of living temperature from 47 and (2) an overestimation of living temperature from oxygen isotopes, which, once the proxies are combined, could lead to (3) an underestimation of living-water δ 18 O sw relative to what would be expected from equilibrium precipitation. Kinetic effects and, by extension, shell growth rates could explain the second order of isotopic variability in brachiopod shells and can be a larger source of isotopic variability than environmental parameters (temperature, δ 18 O sw ) at the local scale.
Figure7. Non-equilibrium fractionation and kinetic effects in brachiopod shell calcite as evidenced by clumped isotopes and oxygen isotopes. (a) Plot of brachiopod inner shell layers in the 47 -1000 ln α (calcite-water) space compared with the equilibrium line (blue line) defined by very-slow-growing abiogenic calcite(Daëron et al. , 2019; Anderson et al. , 2021). The red highlight shows the results from A. kerguelenensis (Crozet Islands) and their expected geochemical values on the equilibrium line. Note the negative correlation between 47 and 1000 ln α (calcite-water) evidenced in this subset. (b) δ 18 O c and 47 offset from their expected equilibrium values calculated from environmental parameters using the equations ofDaëron et al. (2019)  and Anderson et al. (2021) , respectively. The trends followed by these offsets within our new dataset are similar to those reported from other brachiopod samples by Bajnai et al.
Acknowledgements. The authors gratefully thank the leaders and teams of the oceanographic vessels of the following expeditions: the Walda cruise in the southeast  Atlantic Ocean  ( Michel Segonzac, IFREMER, Brest ); sampling from Norway coasts (courtesy of the  Trondheim Biological Station ); MD 30 BIOMASS around the  Crozet Islands (Patrick Arnaud, Marseille, France ; and  Nadia Ameziane ,  MNHN , who provided the material); Musorstom 4, SMIB 3 and 6, and  Norfolk 1 around Loyauté and Lifou islands in New Caledonia  ( Bernard Laurin ,  Université de Bourgogne , Dijon; and  Bertrand Richer de Forges ); Karubenthos 2 around the  French Caribbean island of Guadeloupe  ( Philippe Bouchet  with  Pierre Lozouet ,  Laure Corbari  and  Philippe Maestrati , with the help of  Jerôme Mainguy ,  MNHN, Paris, France ) with a supplementary sampling ( Dominique Lamy ,  Université des Antilles et de la Guyane ); additional material from  New Zealand  ( Daphne Lee ,  Marine Biology Lab, University of Otago ;  David I.
MacKinnon ,  University of Canterbury, Christchurch, New Zealand ; and  Anthony E. Aldridge ; CEAMARC off the  French Antarctic area (Nadia Ameziane  and  Marc Eléaume ,  MNHN, Paris, France ); and sampling from  Palmer Peninsula ,  Antarctic (courtesy of Thomas Desvignes, USA). We thank  Adrian Immenhauser  and two anonymous reviewers for their constructive comments that helped improve the quality of this paper. Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. ANR-18-CE31-0020 ). Review statement. This paper was edited by  Tina Treude  and reviewed by  Adrian Immenhauser  and two anonymous referees.
Code availability. No code has been developed for the purpose of this study. Statistical analysis was done using RStudio build 492. The R package IsoplotR version 4. 4 (https://github. com/pvermees/ IsoplotR, Vermeesch, 2022) was used for its York regression function. Clumped isotope data were processed using the D47crunch Python package (https://doi. org/10. 5281/zenodo. 6300900, Daëron, 2022). Data availability. The new dataset generated for this study is provided in the Supplement. The WOA 2018 dataset used in this study is available on the website of the National Centers for Environmental Information: National Oceanic and Atmospheric Administration (https://www. ncei. noaa. gov/access/ world-ocean-atlas-2018/, last access: 31 March 2023). A version of the dataset to be viewed with Ocean Data View (ODV) is available at https://odv.
awi. de/data/ocean/world-ocean-atlas-2018/ (Ocean Data View, 2023). The dataset of  Bajnai et al. (2018)  used in this study is available from the Supplement of the original publication (https://doi. org/10. 1038/s41598-017-17353-7). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-20-1381-2023-supplement.
Timecourse of Hox gene activation in stembryos In gastrulating mouse embryos, Wnt signaling contributes to the formation of the primitive streak from epiblast cells. Likewise, in stembryos cultured as described in ref. 34, a pulse of the Wnt agonist Chiron 48 h after aggregation of mES cells, that is, between 48 h and 72 h, triggers the differentiation of these epiblast-like cells to form a multilayered structure resembling a posterior elongating body axis. We generated a 12-h timecourse chromatin immunoprecipitation followed by sequencing (ChIP-seq) dataset for H3K27ac, a chromatin mark found at active enhancers and associated with transcription (Fig. 1a-c ), and analyzed the various H3K27ac profiles over the HoxD cluster and its flanking centromeric and telomeric regulatory domains (C-DOM and T-DOM, respectively), two topologically associating domains (TADs) with the latter being split into sub-TADs 1 and 2 containing enhancers of various specificities (for example, ref.
Dynamics of chromatin topology We looked at chromatin dynamic and produced a 24-h time-series of capture Hi-C (CHi-C) datasets, from 48 h, that is, before activation (Fig. 3a ). Interactions were observed among Hoxd genes themselves, likely due to their coverage by H3K27me3 (refs. 19,43) leading to a 'negative' micro-TAD (Fig. 3a ). Also, the cluster displayed several interactions with T-DOM, particularly with sub-TAD1 (Fig. 3a , arrows). Contacts established between CBSs in the cluster and sub-TAD1 were quantified (Extended Data Fig. 4a, b ), and CBS1 unambiguously established the strongest contacts of all CBSs within the HoxD cluster (Fig. 3a , white dashed line). After Chiron treatment, this distribution changed and interactions between CBS1 and the sub-TAD1 CBSs (Fig. 3b , 96 h) were no longer the most prominent.
3c ; compare 48 h with 120 h and see also Supplementary Movie 2). These directional and stepwise increases in contact frequency with sub-TAD1 (or part thereof) are also applied to the Hoxd gene bodies. Hoxd1 to Hoxd4 increased contact frequency with the whole sub-TAD1 at 96 h (Extended Data Fig. 4d ). Hoxd9 did not but increased its interactions with the 3′ region of sub-TAD1 (Extended Data Fig. 4e ), a region rich in H3K27ac potentially reflecting enhancer-promoter contacts. These interactions were also documented by an H3K27ac HiChIP dataset using 120 h stembryos (Fig. 3d ), a stage when anterior genes were activated and cohesin loading was already initiated. The contact map produced a stripe illustrating preferential interactions between the sub-TAD1 region and the active, H3K27ac-positive segment of the HoxD cluster, whereas contacts with sub-TAD2 were much less robust (Fig.
Over time and at a given frequency  46  , extrusion will bypass CBS1 and reach CBS2, which like CBS1 will also start looping toward the CBS sites located within sub-TAD1. The gene located in between will, thus, fall into the positive domain and become activated due to its proximity to T-DOM enhancers (Fig. 4a , bottom, E-P contacts). In turn, this newly transcribed gene will be targeted by NIPBL, and cohesin will now be recruited more posteriorly, in addition to the former domain, thus increasing the probability to bypass CBS2 and hitting yet another CBS. This leads to a self-entertained mechanism progressing toward the posterior end of the gene cluster (Fig. 4b ), following the successive delays imposed by the 'insulating' properties of CTCF sites with an orientation toward sub-TAD1. Our datasets suggest that this model also applies to other Hox clusters, where both the distribution and orientations of CTCF sites are well conserved (Extended Data Fig.
Higher contact frequency is observed between the acetylated region in the HoxD cluster and sub-TAD1 when compared to sub-TAD2, with hotspots matching either the CBS or some acetylated peaks within sub-TAD1 (arrows and arrowheads, respectively). Genomic coordinates-chr2:74190037-75621560. in Hoxd8 transcripts level, which was below that observed when only the first CTCF was mutated (Fig. 5b ). Weakening of these effects was again observed at 120 h, although the gain of Hoxd9 was maintained (Fig. 5d ), along with an expression boundary shifted anteriorly (Supplementary Note 3 and Supplementary Fig. 4 ). At 96 h, a decrease in acetylation over the anterior part of the cluster was observed, as well as a clear gain over the TSS of a long Hoxd4 transcript immediately past the first CTCF site. Gain of acetylation over Hoxd8 was, however, not observed, whereas a weak signal only was gained over Hoxd9 (Extended Data Fig.
However, the boundaries of the H3K27me3 domain did not change in mutant stembryos, indicating that CTCF does not interfere with the initial distribution of polycomb marks in mES cells. Despite this severe impact on the timing of activation, a weak tendency to follow a colinear activation was still observed, in particular, at late stages and from Hoxd9 onwards (Fig. 6c ; compare 120-144 h). A time series of CHi-C datasets with Del(CBS1-5) stembryos showed changes in the dynamic of chromatin architecture compared to controls (Fig. 6d ). First, the Wnt-dependent translocation of the cluster into two domains was no longer observed. Second, at 96 h, the progression of contacts toward more posterior parts of the cluster was not observed either in mutant stembryos. Instead, after Chiron treatment, the anterior part did not clearly segregate from the central and posterior regions and contacts remained distributed throughout (Fig.
Next-generation sequencing analysis and figure generation All NGS analyses, except for single-cell RNA-seq, were performed on a local installation of galaxy  61. The calculations for single-cell RNA-seq have been performed using the facilities of the Scientific IT and Application Support Center of EPFL. All command lines and scripts to regenerate figures are available at https://github. com/lldelisle/ scriptsForRekaikEtAl2022 (ref. 62). Most of the genomic tracks were plotted using pyGenomeTracks version 3. 7 (refs. 63,64) and modified with Illustrator 2022. All boxplots and bar plots were plotted using Prism 9, and heatmaps were plotted with R (www. r-project. org).
RNA-seq Stembryos for each condition were collected and pooled in a 2-ml Eppendorf tube. After centrifugation and medium removal, pelleted stembryos were stored at -80 °C until RNA extraction. RNeasy Plus Micro kit (Qiagen) with on-column DNase digestion was used for RNA extraction following the manufacturer's instructions. RNA-seq library preparation with Poly-A selection was performed with 1 µg of purified RNA using the TruSeq Stranded mRNA kit from Illumina and following the manufacturer's protocol. Library quality was assessed with a fragment analyzer before sequencing on a NextSeq 500 sequencer as paired-end, 75-bp reads. For data analysis, adapter sequences were trimmed from reads using cutadapt  66   ) with default parameters. To reduce the impact of variations in gastruloid growth speed, the FPKM values of HoxD genes (except Hoxd13) and some HoxA genes (Hoxa1, Hoxa5 and Hoxa9) were normalized using FPKM values of genes from other clusters (Hoxa and Hoxc genes were used to normalize Hoxd, whereas Hoxb and Hoxc genes were used to normalize Hoxa; see GitHub repository for more details).
1 chemistry) following the manufacturer's recommendation. Up to 6,000 cells were targeted per sample. cDNA preparations were performed according to 10x Genomics recommendations, amplified for 10-12 cycles and sequenced on a HiSeq 4000 with the cbot2 chemistry. At least two replicates were performed for each timepoint. In addition, a third 120-h timepoint was generated by dissection in two https://doi. org/10. 1038/s41588-023-01426-7 portions to separate the anterior and posterior halves, and each portion was analyzed separately by single-cell RNA-seq. For data processing, the Cell Ranger pipeline version 6. 0 (10x Genomics) was used with the default parameters on each fastq pair of single-cell RNA-seq datasets to perform the alignment, by applying filtering and count barcodes and UMIs. Alignment was performed against the mouse reference genome mm10 and a modified gtf file based on Ensembl version 98 (ref.
72). Each gene-cell count matrix was then analyzed using Seurat version 4. 0. 2 (ref. 73). A Seurat object was generated by filtering out barcodes with less than 200 identified gene and genes identified in less than three cells. Then, further filtering was applied to remove low-quality cells and doublets. For each dataset, the mean RNA count was used, and cells with either less than 40% of the mean or more than 2. 5-fold of the mean were removed. In addition, cells with more than 8% or less than 0. 05% mitochondrial counts were removed. Each dataset was then normalized using the NormalizeData command, the 3,000 most variable features were identified and the cell cycle was scored (using the 2019 updated gene list from Seurat). Then, all individual datasets were merged into a single Seurat object.
Final DNA libraries were purified and size selected using AMPure XP magnetic beads (Beckman Coulter), and fragment analysis was performed before sequencing on a NextSeq 500 sequencer as paired-end, 75-bp reads. For data analysis, fastqs of the three replicates of wt_144 h_pSer2PolII were concatenated before any processing. Adapter sequences and bad quality bases were trimmed from reads using cutadapt  66   and 10a , the profiles of mutant and corresponding wild-type were corrected using the timecourse experiment and the profile at the HoxA cluster as a guide (see GitHub repository for more details). The profiles were then averaged for the two replicates, and differences were performed using the bash script available in the GitHub repository. Motif analysis of CDX2 ChIP-seq peaks in Supplementary Fig.
CHi-C Collected stembryos were pooled and fixed for 10 min in 1% formaldehyde at room temperature and stored at - ). For 48 h, 96 h and 144 h, in those wild-type samples where three replicates were available, raw matrices were summed before further processing. The matrices were balanced with cooler balance (--cis-only). For the Del(sub-TAD1) mutant, the balancing was done with the option-ignore-diags 5 instead of --ignore-diags 2 to discard the bias due to the presence of a BAC containing the region (mm10:chr2:74747769-74913171). In Fig. 3c , the histograms were produced with a Plot profile on ImageJ version 2. 1. 0/1. 53c. Virtual 4C was computed using the same script as in ref. 28 inspired by the method described in ref. 79 (whenever multiple replicates were available, valid pair files were concatenated).
b, Virtual4C contacts generated from Capture Hi-C datasets produced out of control and Del(CBS1-5) mutant stembryos at 96 h. Interactions are quantified between those Hoxd genes shown below (viewpoint) and the activated Hoxd3-Hoxd4 part of the cluster. Increased contacts are scored in mutant stembryos for all three viewpoints, illustrating the loss, in CTCF mutant stembryos, of a clear partition of the gene cluster into two separate positive and negative domains. c, Time course capture Hi-C using Del(CBS1-5) stembryos. Six time points are shown with bin size 5 kb. Libraries were mapped on a control genome (mm10). Genomic coordinates: chr2:73900037-75621560. d, Magnifications of the interactions between the Hoxd genes cluster and the CS38-40 region (dashed box) in control (left) and Del(CBS1-5) mutant specimen (right), either before(48 h, top) or after (96 h, bottom) the translocation of the anterior part of the cluster.
Diagonal and vertical dashed lines indicate the position of the most prominant interactions within the Hoxd gene cluster with the CS38-40 region. While a shift toward a posterior position is observed between 48 h and 96 h in control stembryos (from CBS1 to the Hoxd8-Hoxd9 interval), interactions scored in the Del(CBS1-5) mutant condition remain diffused over the gene cluster and only a slight change in the posterior limit is observed (around Hoxd11). Genomic coordinates: chr2:74590000-74985000 e, Virtual4C contacts generated from Capture Hi-C datasets produced in control and Del(CBS1-5) mutant stembryos at 96 h. Interactions between the indicated Hoxd genes (viewpoints) and the 5′ region of sub-TAD1 are significantly higher in Del(CBS1-5) mutant. In b and e, box plots with median value and 25-75% percentiles, whiskers represent minimum and maximum.
Acknowledgements We thank  F. Darbellay  for advice with ChIP-M,  C. Bolt  for his assistance with CHi-C experiments,  M. Anselmet  for her help with RNA-seq experiments and other colleagues from the  Duboule laboratories  for discussions. We are grateful to  A. -C. Cossy  for her technical assistance with cell culture. This work was supported by funds from the  École Polytechnique Fédérale de Lausanne  ( EPFL, Lausanne ), the  University of Geneva , the  Swiss National Research Fund  ( 310030B_138662  and  310030B_138662  to D. D. ) and the  European Research Council  grant  RegulHox  ( 588029  to D. D. ). Funding bodies had no role in the design of the study and collection, analysis and interpretation of data and in writing the manuscript. This work was supported in part using the resources and services of the  Gene Expression Research Core Facility (GECF) at the School of Life Sciences of EPFL.
a, H3K27me3 ChIP-seq using control (top, light brown) and Del(CBS1-5) (middle, dark brown) stembryos at 96 h (average of two replicates). The subtraction track is shown below and illustrates the decrease in H3K27me3 marks over the central and posterior part of the HoxD cluster (bracket). Genomic coordinates: chr2:74646423-74780095. b, Virtual4C contacts generated from Capture Hi-C datasets produced out of control and Del(CBS1-5) mutant stembryos at 96 h. Interactions are quantified between those Hoxd genes shown below (viewpoint) and the activated Hoxd3-Hoxd4 part of the cluster. Increased contacts are scored in mutant stembryos for all three viewpoints, illustrating the loss, in CTCF mutant stembryos, of a clear partition of the gene cluster into two separate positive and negative domains.
Genomic coordinates: chr2:74590000-74985000 e, Virtual4C contacts generated from Capture Hi-C datasets produced in control and Del(CBS1-5) mutant stembryos at 96 h. Interactions between the indicated Hoxd genes (viewpoints) and the 5′ region of sub-TAD1 are significantly higher in Del(CBS1-5) mutant. In b and e, box plots with median value and 25-75% percentiles, whiskers represent minimum and maximum. Asterisks represent significant changes determined by the Mann-Whitney nonparametric test (**** is p-value < 0. 0001) (wt: sum of 3 independent replicates, Del(CBS1-5) mutant: single replicate).