dataesr/scientific-paragraphs-categorization huggingface dataset:
Widespread epigenetic modulation facilitates transcription factor driven differentiation Based on the observed extensive transcriptional regulation during enterocyte differentiation, we hypothesized that global remodeling of the epigenome must precede or at least accompany rewiring of the transcriptome and proteome. To investigate this, we used ATAC-seq to profile DNA accessibility and we performed ChIPmentation to map histone modifications H3K27ac, H3K4me3, and H3K27me3 in the organoid cultures. A total of 94,340 peaks could be detected in at least one replicate of the different sequencing datasets, which we used to identify genomic regulatory elements that undergo a significant change in DNA accessibility or histone modification occupancy between the different cell-type-enriched organoid cultures  (Fig 3A, Dataset EV4).
Overall, these observations illustrate the importance of system-wide interrogation and the power of integrative analyses to uncover novel biology related to intestinal homeostasis. ◀ Figure  3. Widespread modulation of epigenetic landscape for differentiated organoids is driven by Hnf4g. A Heatmaps showing epigenome dynamics on peaks with significant changes in DNA accessibility and histone modifications between CV, ENR, and EN organoids. Heatmaps are split on their location near a transcription start site (< 500 bp from TSS, top heatmaps) or other location. Heatmaps are further subdivided based on the dataset it was found significantly changing in (shown at the left).
Discussion Here, we presented an integrative systems biology approach that in combination with organoid technology can disentangle the complex regulatory networks that underlie lineage specification in the mouse small intestine. Our study shows that profiling the epigenome, transcriptome, proteome, and metabolome represents a powerful approach to obtain a system-wide overview of the regulatory mechanisms, which enable gene expression regulation in cell-typeenriched organoid cultures. We show that, while each single technique is informative on its own, performing multiple complementary techniques will strengthen each other such that completely new questions can be answered after integrating a system-wide dataset. The two major bottlenecks for employing a large set of "omics" techniques to study epithelial homeostasis in the intestine are (i) the need for large quantities of cellular material for high-resolution data generation.
While the focus of this study resulted in the discovery of a transcription factor as a regulator of intestinal stem cell differentiation, different analyses can be performed on our datasets as a resource to uncover additional layers of regulation, for example, when focusing on cell-type-specific metabolic processes or chromatin remodeling complexes. Indeed, we show that changes in the lipidome during differentiation reveal a change in mitochondrial load and energy metabolism. However, future efforts are needed to investigate the importance of other metabolic changes we observed, such as the drastic increase in glutathione, an important endogenous antioxidant capable of regulating redox signaling  (Circu & Aw, 2012) , and the global decrease in amino acids, which can affect post-transcriptional regulation  (Zoncu et al, 2011).
Mass spectra were collected on a LTQ-Orbitrap Fusion Tribrid mass spectrometer (Thermo) in data-dependent top-speed mode with dynamic exclusion set at 60 s. Raw mass spectra were analyzed in MaxQuant 1. 5. 1. 0 (Cox & Mann, 2008)  with match-between-runs, iBAQ, and label-free quantification enabled. A mouse RefSeq protein sequence database downloaded at 28-06-2016 from UCSC was used to identify proteins. Identified proteins were filtered for reverse hits and potential contaminants. Proteins detected in the BME gel were blacklisted for downstream analyses. We only considered proteins that were identified in at least all of the triplicates of the same sample, for downstream analyses. Missing values were semi-randomly imputed based on the assumption that they were not detected because they were under the detection limit of the mass spectrometer (in Perseus, MaxQuant software package).
Integration of proteome and transcriptome data To accurately integrate the protein and transcript expression data, we used matched proteome and transcriptome RefSeq databases and quantified expression as described above. To ensure correct matching of isoforms, we restricted the transcriptome database to only transcripts that were already quantified at the protein level. Significantly regulated proteins were identified with an adapted Student's t-test on LFQ values while controlling for the multiple testing hypothesis with a 10,000× permutation-based false discovery rate [Perseus, MaxQuant software package  (Cox & Mann, 2008) ]. To calculate the amount of proteins per mRNA transcript, we performed absolute quantification of both mRNA and proteins as described above. Next, we calculated the protein over mRNA ratios by comparing all replicates to each other and taking the median ratio.
ChIPmentation sample preparation Chromatin from 80,000 to 150,000 cells was used for ChIPmentation. ChIPmentation was performed as described earlier  (Schmidl et al, 2015)  with several modifications. In short, the chromatin was incubated overnight at 4°C rotating in dilution buffer (1% Triton X-100, 1. 2 mM EDTA, 16. 7 mM Tris pH 8, 167 mM NaCl), 1× Protease Inhibitor Cocktail (Roche), and 1 lg of antibody [H3K4me3 (C15410003, Diagenode)/H3K27ac (C15410196, Diagenode)/ H3K27me3 (C15410195, Diagenode), Hnf4g (Sigma, HPA005438)]. Per ChIP 10 ll protein A Dynabeads and 10 ll protein G Dynabeads were used. Beads were washed using dilution buffer (+0. 15% SDS, +0. 1% BSA) and incubated with the chromatin and antibody mix for 60 min at 4°C rotating. Afterward, the beads were washed at 4°C once with ChIP wash buffer 1 (2 mM EDTA, 20 mM Tris pH 8, 1% Triton X-100, 0.
Transcription factor selection To identify transcription factors that are most likely to explain observed dynamics in our DNA accessibility and ChIPmentation data, we used a non-redundant vertebrate motif database (https:// doi. org/10. 6084/m9. figshare. 1555851) and selected motifs on transcription factor protein expression, and by LASSO and random forest feature selection. First, we reasoned that transcription factors that are not found in our deep proteome data are unlikely to contribute to gene expression. LASSO and random forest regression were used to select the best candidate motifs to explain the observed epigenetic changes. LASSO reduces the number of features while maintaining high prediction accuracy. Random forest is an easily interpretable and robust machine-learning method that is able to identify non-linear relations and internally controls for overfitting.
Tissue Isolation and histology Two-to six-month-old mice were euthanized, and jejuna were removed and flushed gently with phosphate-buffered saline (PBS). For histological analyses, pieces of proximal jejunum (1 cm) were immediately fixed overnight at 4°C in paraformaldehyde 4% before embedding in paraffin. Periodic acid-Schiff staining was performed by using a standard histological protocol  (Cattin et al, 2009). Staining was examined by bright field microscopy using an Axiophot microscope connected to an Axiocam camera using the Axiovision 4. 5 software (Zeiss). Goblet cells (PAS-positive cells) were counted in villi on one section of jejunum from WT and Hnf4g KO mice without mouse or tissue selection exclusion.
Western blot For Hnf4g expression in WT and KO organoids: Organoid pellets were resuspended in 100 ll whole cell lysis buffer per well of a 6-well plate (150 mM NaCl, 50 mM Tris pH 8, 10% glycerol, 1% NP-40, 1 mM DTT, 1× CPI). Extracts were rotated for 1. 5 h at 4°C followed by seven cycles of 30 s on and 30 s off sonication (Biorupter Pico, Diagenode). Afterward, the samples were spun at 16,200 × g for 5 min at room temperature and the supernatant was stored at À80°C. Samples were loaded on an 8% SDS-PAGE gel and afterward transferred to a nitrocellulose membrane. Membranes were incubated with primary antibody in 5% milk/TBST overnight at 4°C [anti-beta actin 1:5,000 (Abcam, ab16039), and anti-HNF4G 1:500 (Sigma, HPA005438)]. Afterward, membranes were incubated with secondary antibody [Polyclonal Swine Anti-Rabbit Immunoglobulins/HRP 1:3,000 (Dako, P0399)] and imaged using chemiluminescent substrate  (Thermo Fisher, 34580).
Shown P-values are corrected for multiple testing hypotheses with the BH method. B Clustered genomic regions from (A) are shown. Regions were associated with their single closest TSS and corresponding mRNA expression changes are shown in the most left heatmaps. The best match score for the HNF4G motif is shown in the middle heatmap. Hnf4g binding is shown in the most right heatmaps. C Scatterplot with the transcription factors that are most likely to explain the dynamics shown in (A). The x-axis is the linear correlation between the best motif score and the log 2 -fold change EN over ENR under each changing peak for a given dataset as shown in (A). The y-axis is the feature importance of the motif in a random forest model in percentage increase in mean-squared error (MSE), where motifs that are important for the model to accurately predict the given fold changes get a high error increase.
Different datasets are color-coded. For motifs with more than one transcription factors associated to it, we show the transcription factor name with the most significant change in protein expression. The vertical line over each dot represents two times the standard deviation of the MSE. D Heatmaps showing change in protein (left) and mRNA (right) expression of selected transcription factors, compared to ENR organoids. Data information: In panels (A and B), relative changes between different organoids are shown as log 2 row mean subtractions (RMS) where the signal is compared to the row mean of all samples. ª 2018 The Authors Molecular Systems Biology 14: e8227 | 2018.
Acknowledgements We thank all the members of the  Vermeulen laboratory  for fruitful discussions. We thank  F. de Sauvage  ( Genentech ) for providing organoid cultures of Lgr5-DTR-GFP knock-in mice,  Deltagen Inc. , for supplying the Hnf4g +/À mice,  Eva Janssen-Megens  from the  Radboud University  for help with next-generation sequencing, and  Dr. Edwin Stigter  from the  Medical Metabolomics Facility UMCU  for performing LC-MS/MS-based metabolomics. The Vermeulen, Snippert, and Burgering laboratories are part of the  Oncode Institute  which is partly financed by the  Dutch Cancer Society (KWF). Furthermore, work in the  Vermeulen and Burgering laboratory  is funded by the gravitation program CancerGenomiCs. nlfrom the  Netherlands Organization for Scientific Research (NWO).
Since two decades the richness and potential of natural history collections (NHC) were rediscovered and emphasized, promoting a revolution in the access on data of species occurrence, and fostering the development of several disciplines. Nevertheless, due to their inherent erratic nature, NHC data are plagued by several biases. Understanding these biases is a major issue, particularly because ecological niche models (ENMs) are based on the assumption that data are not biased. Based on it, a recent body of research have focused on searching adequate methods for dealing with biased data and proposed the use of filters in geographical and environmental space. Although the strength of filtering in environmental space has been shown with virtual species, nothing has yet been tested with a real dataset including field validation.
In order to contribute to this task, we explore this issue by comparing a dataset from NHC to a recent targeted sampling of the cockroach genus Monastria Saussure, 1864 in the Brazilian Atlantic forest. We showed that, despite strong similarities, the area modeled with NHC data was much smaller. These differences were due to strong climate biases, which increased model's specificity and reduced sensitivity. By applying two forms of rarefaction in the environmental space, we showed that deleting points at random in the most biased climate class is a powerful way for increasing model's sensitivity, so making predictions more suitable to the reality.
This recent emphasis on NHC data also brings lots of benefits for studies of macroecology. The international enterprise of rendering available data from specimen's labels (and associated information from field notes and expedition logs), and more recently, traits and pictures of the specimens, is powering this research field, which is becoming central in ecology and biodiversity conservation  [13]. The massive amount of data available in national databases and some data federators like GBIF (http://www. gbif. org) along with environmental data interpolated at high spatial resolution (e. g. [14, 15] ) and powerful methods of analysis does not only allow for unraveling main patterns of biodiversity distribution, but also for understanding the processes leading to them (see  [13]  for a review).
g. [23, 25]. This makes that the use of NHC data is very challenging, particularly because ENMs as estimated in from presence-only models  [26]  are based on the assumption that distribution records are not biased  [27]. Due to this, a whole body of research has been devoted to the characterization of biases in collection databases and to the search of solutions in order to minimize errors on estimates based on ENMs  [28, 29]. However, the lack of field validation still represents a major constraint for evaluating and understanding models' outcomes (  [10, 30] ). Field data is very necessary for confirming distribution, assessing eventual biases in the samples from NHC, so allowing to go a step further and developing solutions for using them in biodiversity assessments. During a biogeographic study in the Brazilian Atlantic forest, we took advantage of a longterm survey of the insect genus Monastria Saussure, 1864 (Dictyoptera, Blaberidae) to mobilize data for this kind of study.
We referred to all Museum collections in the world that harbored specimens of Monastria and we conducted a field sampling designed to characterize their distribution in the biome and to define the limits of their distribution range. The main interest of focusing on species of this saprophagous genus is that they are not specialized, so not constrained by specific resources like a host plant  [31, 32, 33] ). They typically represent that important fraction of biodiversity that is actually not well-known or even followed on a regular basis, contrarily to some vertebrates, and therefore necessitates that all available data are mobilized for its study  [34]. Here we used all distribution records available to the species of this genus aiming to explore whether data issuing from NHC dataset would be enough to predict its entire distribution range, as validated by the recent sampling dataset.
Based on it, we explored how sampling biases could be responsible for the result. Then, we developed two strategies of rarefaction and compared the way they influenced the outcomes of ENMs. The study was made in the Brazilian Atlantic forest, a diverse forest ecosystem, comprising several different physiognomies. Our main expectation is that the comparison of samples from NHC with present sampling will unravel trends commonly found in NHC datasets, so allowing to explore what leads to them, and some ways to deal with them if we aim to produce sound biodiversity assessments.
Collection data We searched for Monastria in collections of Natural History Museums (NHM) and in the literature. The survey in NHM was made through contacts and specific requests to the curators of the main repositories of Neotropical fauna in the world. This was very often complemented by exchanges of pictures in order to specify the cockroaches we were looking for. Concerning the literature, we relied on the catalogue of  [40] , and the updates available on the Taxonomic Catalogue of the Brazilian Fauna at http://fauna. jbrj. gov. br/fauna/listaBrasil/ConsultaPublicaUC/ ResultadoDaConsultaNovaConsulta. do, which provides an exhaustive and updated survey of the publications on the Blattaria from Brazil. This led to a dataset issuing from 23 references (S1 Appendix) and 11 collections (S1 Table ).
Climate data We used Bioclim variables obtained in WORLDCLIM Version 1. 4 database (http://www. worldclim. org; [14] ), in 30-arc second resolution, or about 1km x 1 km near the equator. In order to reduce collinearity (e. g. [28] ), we eliminated variables where Pearson's r >0,80 and retained the ones correlated with more variables. So, the analysis was limited to only eight of them (Table  1 ).
We transformed this probability field into binary maps of "suitable" (upper class) versus "unsuitable" for calculating and comparing the distribution area. These maps were transformed into polygons used to calculate the final area with ArcGis 10. 4. The Area Under the Table  1. The eight bioclim variables used in this study. Abbreviation, full name, minimum and maximum values of the occurrence records from the target sampling (TS), and natural history collections and literature (NHC) dataset. The last columns present the difference between the two datasets and the sum of these differences. Curve (AUC) on Receiver Operating Characteristis (ROC) plots of training and test was used to validate the models. In order to avoid problems in comparisons of these estimators the geographic extent of the models was always the same  [45].
Assessing biases and analyzing its effect in the dataset The distribution of sampling points in the dataset was assessed in two ways. The first was the estimation of the aggregation of points in the geographic space. It was tested with Averaged Nearest Neighbor calculated in ArcGis 10. 4. This test verifies if distances between nearest neighbors are different than what would be expected if they were at random. The second was the evaluation of sample aggregation in climate space, i. e. , if samples were aggregated in places having a certain type of climate in common even when these places were scattered apart geographically. This was done through the assessment of differences in probability of occurrence between observed and expected number of points (  [17, 24]. Following the basic MaxEnt output, the climate space was divided into 9 equal-interval bins based on the range observed within the Atlantic Forest.
In other words, this formula compares the number of samples observed with that expected, assuming that the probability of being collected in a fraction of the climate is proportional to the total area comprised by it. In order to check the implications of climate biases on the ENMs of collection data we designed a rarefaction strategy to delete points in order to make subsets of the dataset. We limited this analysis to Annual Precipitation based on the fact that this variable is the one with greatest difference in range covered between the niches with the two datasets. Two forms of rarefaction were employed. In the first we eliminated 30%, 40%, 45% and 55% of the points from the most skewed climate class (11, 15, 17 and 21 points, respectively) chosen at random. In the second, we deleted the same number of points at random from the entire dataset.
Characterization of the datasets Our dataset was composed of 82 occurrence data: 56 from Museum collections and literature (hereafter NHC) resulting from 23 independent samples (S1 Appendix), and 26 from the called target sampling (TS). Twenty-one additional locations were studied with the target sampling without finding Monastria. As most of these absences looked associated to the present forest degradation, they were not used as pseudo-absences. Both occurrence records cover about the entire range of the Atlantic forest. But NHC dataset includes records much further in the South and West whereas the TS dataset includes presences in the extreme Northeast (Fig  1 ). Despite these differences in the geographical space, the range of the occurrence in environmental space is quite similar. As can be seen by the sum of the differences between minimum and maximum values with the two datasets, annual precipitation is the variable with the highest difference of range (Table  1 ).
Assessing distribution with the two different datasets MaxEnt performed well in both analyses. The training AUC (area under the receiver operating characteristic curve) was slightly higher for the ENM with collection data (0. 9429), than in the ENM with data from the target sampling (0. 9381). In both cases it strongly rejected the hypothesis that test points were predicted no better than by a random prediction. No locality point fell outside the total distribution area predicted by the model, although some of them were found in areas with low predicted suitability. The I-statistics indicates that the entire area of ENMs estimated with the two datasets strongly overlap (I = 0. 92) (Fig  2 ). The analysis of contribution of the different variables indicated that Bio02 was the one with highest regularized trained gain, with 31.
1% and 29. 2%, followed by Bio03 and Bio14 for collection and target sampling, respectively. It shows that the most suitable areas for Monastria were those with low mean diurnal range in temperature (Bio02 and Bio03), which, in this region, was mainly determined by variations in precipitation during the driest month (Bio 14) (Table  2 ). In spite of this, the ENMs differed markedly in extent of suitable area. The range estimated with NHC data corresponded to only 67% of that with our recent sampling, indicating suitable areas much concentrated in the humid forests at the central region of the biome, particularly in the region of Rio de Janeiro. The model produced with the TS dataset showed additional suitable areas in the Northeast, where Monastria was not known before. Another important difference was detected in the extreme South at the interior of Rio Grande do Sul, both with several records in the NHC dataset, but not identified as suitable with the model produced with it (Fig  2 ).
Assessing biases in the datasets The test of spatial aggregation showed that, although values were significant for both datasets, they were much higher in the data from NHC (Z-score = -5,892; p< 0,0001) than that in the target sampling (Z-score = -2,2901; p = 0,022). It means that the observed average distance between points was much lower than expected at random, especially in the NHC dataset. The analysis of climatic biases shows that the intermediate climate class 4 was the most sampled in both datasets. Nevertheless, biases were much higher (more than twofold) with data from NHC than with data from the TS dataset, particularly for Bio2, Bio5 and Bio12 (Table  3 ).
Effect of rarefaction on the collection dataset Since Bio12 was the environmental variable with highest difference in range between the two models (Fig 4 ) we chose to use it to test the effect of rarefaction on the environmental space. As expected, AUC values were significantly reduced with rarefied data, especially AUC training (One-way ANOVA F = 4. 4185 p<0. 0001 DF = 8) but also for AUC test (F = 2. 9906; p = 0. 004; DF = 8). But, the estimated suitable areas were significantly higher (F = 11. 72348 p<0. 0001 DF = 8). The comparison of two ways of rarefaction showed important differences concerning AUC training and area. AUC training varied markedly and not linearly when the dataset was  rarefied by deleting points in the most biased climate class. But, when 21 points was deleted, the values from the two modes of rarefaction were very similar and also similar to the that estimated with all the NHC dataset.