dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2008; Monteys et al. , 2010)  of miRNA have independent promoters. The location of miRNA promoters was also inferred from CAGE data (Cap analysis gene of expression)  (Chien et al. , 2011; Marsico et al. , 2013). Recently, de Rie and coworkers as part of the FANTOM5 project (Functional Annotation of Mammalian Genome) have combined CAGE and RNA-seq data to locate TSS and the 5' end of pri-miRNAs in many tissues and cell types. They estimated that there are 1,357 promoters for humans and 804 for mouse miRNAs  (de Rie et al. , 2017). However, these putative regulatory regions were not functionally validated. CRISPR (Clustered regularly interspaced short palindromic repeats)/Cas9 activation (CRISPRa) and inhibition (CRISPRi) are two techniques that take advantage of a dead Cas9 (dCas9) that is fused to either activators or repressors of transcription.
Development of a pipeline to test the activity of putative miRNA promoters with CRISPRa and CRISPRi To test the function of regulatory regions on miRNA expression, we designed an experimental pipeline in four steps (Figure  1 ). The first step consists in locating active promoters using CAGE and/or RNA-seq data, which are either homemade or publicly available from consortiums, including the FANTOM5 consortium (de Rie et al. , 2017). In the example depicted in Figure  1 , the host gene has two promoters and the intronic miRNA has one promoter. FANTOM5 CAGE data (de Rie et al. , 2017) peaks colocalize with promoter 1 (host gene) and the intronic miRNA promoter, showing that these promoters are active in this cell type. RNA-seq data confirms that promoter 1 of the host gene is active. The FANTOM5 further predicts that the pri-miRNA originates at promoter 1 of the host gene.
CRISPRi-mediated repression of Mest promoter suppresses the expression of hosted miR-335 in embryonic stem cells We first applied our pipeline to miR-335. miR-335 is located in an intron of the protein-coding gene Mest (Figures  2A, B ). miR-335 is transcribed from the same DNA strand as its host gene, a common feature of intronic miRNAs  (Hinske et al. , 2014). Mest and miR-335 are highly conserved during evolution and frequently co-regulated  (Liang et al. , 2007; Ronchetti et al. , 2008; Tomé et al. , 2011; Yang et al. , 2014; Hiramuki et al. , 2015). This suggests that Mest and miR-335 share common regulatory sequences. Mest has one distal promoter (D) and one proximal promoter (P) (Figure  2A ). In addition, the PROmiRNA and FANTOM5 databases (Marsico Step 1: The putative promoters are inferred from CAGE data (here from the FANTOM5 consortium, which also predicts pri-miRNA transcription start), and/or RNAseq data.
Transactivation of the promoters of miR-3662 or host gene HBSIL increases miR-3662 levels Next, we sought to extend our approach to another intronic microRNA, hsa-miR-3662 (hereafter: miR-3662). miR-3662 is a human-specific miR that plays bivalent roles in cancer, acting either as a tumour suppressor or an oncogene in different cellular contexts  (Powrózek et al. , 2015; Maharry et al. , 2016; Powrózek et al. , 2017). We recently reported that MIR-3662, located in an intron of the HBSL1 gene, is tunable by CRISPRa/i targetting of an intronic sequence in HBSL1  (Yi et al. , 2022). The FANTOM5 database  (de Rie et al. , 2017)  predicts from CAGE data that MIR-3662 uses the promoter of its host gene HBSL1. Thus, we next investigated whether the expression level of miR-3662 is also tunable by activating the promoter of HBS1L.
Discussion Here we show that CRISPRa and CRISPRi, where dCas9 is fused to transcriptional activators or repressors, respectively, are powerful tools to test genomic regions predicted to be microRNA promoters in databases such as those released by the FANTOM5 consortium  (de Rie et al. , 2017). Others have previously used CRISPR-based approaches to manipulate miRNA levels and/or map their promoters. As early as 2014, Zhao and colleagues observed that dCas9 targeted to the upstream sequence of the miR cluster miR17-92 leads to the repression of miR-19a, miR-20a and miR-92  (Zhao et al. , 2014). In these experiments, dCas9 was not fused to a transcriptional repressor. Thus, Zhao and colleagues hypothesized that dCas9 sterically blocked RNA PolII (hence they have used the term CRISPR interference and not CRISPR inhibition)  (Zhao et al.
, 2014). More recently, Drobna-Śledzińska and colleagues have developed a complete pipeline from microRNA TSS prediction to sgRNA design and CRISPR inhibition. TSS prediction was inferred from four databases (miR start, DIANA-miRGen, CRISPR tool and FANTOM 5)  (Drobna-Śledzińska et al. , 2022). Here, as described by Drobna-Śledzińska and colleagues, we have used FANTOM 5 as a prediction tool for promoter activity and CRISPRi. We have further completed the prediction of FANTOM5 with RNA-seq experiments. Prediction tools, including FANTOM5, predict active promoters. Hence, these tools are useful to design sgRNAs for CRISPRi experiments. In addition to CRISPRi, we also performed CRISPRa experiments. We observed that CRISPRa did not upregulate Mest RNA when the SAM complex targeted the Mest proximal promoter (predicted to be active in mESCs by FANTOM 5).
By contrast, CRISPRa was effective when targeting the inactive Mest promoter (hence absent from the FANTOM5 database). Collectively, this suggests that sgRNAs for CRISPRi should target the active promoter (predicted in databases such as the FANTOM5 database), while for CRISPRa it is worth targeting all of the known promoters, especially those that are inactive. This also likely depends on the basal level of expression and cell type. Indeed, our data obtained in MEFs also revealed that transactivating the (P) promoter resulted in a strong increase in Mest and miR-335 while transactivating the (D) promoter had moderate effects. This contrasts with results obtained in ESCs where the most potent sgRNAs were those targeting the (D) promoter (Figure  4  and Supplementary Figure  S3 ). Thus, taken together, these data suggest that transcriptional activation of one or the other Mest promoter is sufficient to increase the levels of intronic miR-335.
, 2014). Collectively, this suggests that the expression of miR-335 only depends on the activity of the promoter of Mest. We cannot rule out that miR-335 has an independent promoter located in another region or is active in another cell type. In this context, the prediction of miR-335 promoter location made by the PROmiRNA database  (Marsico et al. , 2013)  suggests that there could be several miR-335 promoters depending on the tissue, the most probable being Mest (D) and (P) promoters -what we confirmed experimentally here-, and less probably, a third region situated in another intron of Mest. It is also possible that additional regulatory sequences, including enhancers situated at long distances, influence the expression of miR-335. In this context, it will be interesting to screen the Mest locus along several Mbs with a wider range of sgRNAs, not only with the CRISPRa and CRISPRi molecular platforms as done here but also with dCas9 fused to enhancer regulators  (Li et al.
Acknowledgments We thank  Laurent Journot  for helpful discussions and suggestions;  Lauriane Fritsch ,  Boyan Bonev , and  Giacomo Cavalli  for  SAM ESCs and SAM MEFs ;  Hervé Seitz ,  Adrien Décorsière , and  Laure Garnier  for critical reading of our initial draft manuscript;  Chris Planque  for advice;  Philaé Gil  and  Pierre Nègre  for technical assistance;  Lionel Quentin  and  Nicolas Boucharel  for performing mycoplasma tests; members of the  IGF  for continuous support. pB-CAGGS-dCas9-KRAB-MeCP2 was a gift from  Alejandro Chavez  and  George Church  (Addgene plasmid #  110824 ). HyperPiggyBac Transposase (pCMV-HA-HyperpBase) was a gift of  Kosuke Yusa  and  Allan Bradley  and was kindly provided by  Thomas Dibling  ( Sanger, Cambridge, United Kingdom ). PB-SAM (Addgene, # 102559 ) was a gift from  Ying Liu.
Prototype implementation. Prototype implementations of the proposed approaches were implemented in Python 9. The PySAT toolkit 10 was used for propositional encodings. Besides, PySAT invokes the Glucose 4 11 SAT solver to pick a weak AXp candidate. SDDs were loaded by using the PySDD 12 package. Benchmarks & training. For SDDs, we selected 11 datasets from Density Estimation Benchmark Datasets 13. [34, 46, 49]. 11 datasets were used to learn SDD using LearnSDD  [11]  (with parameter maxEdges=20000 ). The obtained SDDs were used as binary classifiers. For DLNs, we selected 5 publicly available datasets: australian (aus), breast_cancer (b. c. ), heart_c, nursery  [57]  and pima  [2]. We used the three-layer DLN architecture: Calibrators → Random Ensemble of Lattices → Linear Layer. All calibrators for all models used a fixed number of 20 keypoints.
Acknowledgements. This work was supported by the  AI Interdisciplinary Institute ANITI , funded by the  French program "Investing for the Future -PIA3 " under Grant agreement no. ANR-19-PI3A-0004 , and by the  H2020 -ICT38  project  COALA "Cognitive Assisted agile manufacturing for a Labor force  supported by trustworthy  Artificial intelligence ", and funded by the  Spanish Ministry of Science and Innovation (MICIN)  under project  PID2019-111544GB-C22 , and by a  María Zambrano   fellowship  and a  Requalification fellowship  financed by  Ministerio de Universidades of Spain  and by  European Union -NextGener -ationEU.
3). As can be observed this loose upper bound is respected by the reported results. Dataset %Y Runtime (s) SAT Time SAT Calls κ(v) Time κ(v) Calls κ(v)Time Runtime Max Avg. Max Avg. Max Avg. Max Avg. Max Avg. aus 61 40. 4 8. 31 0. 02 0. 01 291 65 40. 0 8. 15 424 98 97. 8% b. c. 45 5. 4 1. 93 0. 00 0. 00 53 20 5. 3 1. 89 78 30 98. 0% heart_c 35 31. 5 6. 67 0. 02 0. 00 171 54 31. 1 6. 52 249 80 97. 7% nursery 45 4. 3 1. 77 0. 00 0. 00 31 13 4. 3 1. 75 73 30 98. 6% pima 74 3. 7 1. 41 0. 00 0. 00 33 13 3. 7 1. 39 47 22 98. 4% ferent size).
We described the frequency of tweets with a timeline. We performed a topic modeling based on the unsupervised probabilistic Latent Dirichlet Allocation model (LDA). We used the Python libraries Gensim to perform topic modeling and pyLDAvis to visualize the clusters and the keywords for each topics  [4]. Finally, we have extracted the posts from the Twitter account @Orpea_. The activity of the account was described by the number of posts published.
T5168), anti-Actin (Sigma, cat. A2228) anti-Flag (Sigma, cat. F3165), anti-SAMD9L (Proteintech, 25173-1-AP), anti-SAMD9 (Sigma, HPA021319), anti-Gag (NIH HIV Reagent Program, 183-H12-5C), anti-Env (Aalto, D7324), secondary anti-mouse and anti-rabbit IgG-Peroxidase conjugated (Sigma, cat. A9044 and AP188P, respectively). Total protein was also used as loading control, using the PrestainedTM BioRad gels. Virus release titrations by ELISA or RT assays. Titration of HIV-1, as well as HIV-2 and SIV, viruses in the supernatant were performed using HIV-1 p24 and SIV p27 ELISA kits (XpressBio), respectively, following the manufacturer's instructions. Reverse transcriptase RT activity of retroviruses in viral supernatant was determined with an exogenous RT reaction, in which the ability of RT to incorporate 32 P-labeled dTTP into a poly(rA)/oligo(dT) matrix is quantified  50.
Structure homology analyses. HHpred was used for homology detection analyses  51. Protein structures were retrieved from AlphaFold DB and RCSB PDB  [52] [53] [54]. Structural analyses and visualization were performed using UCSF Chimera (The Regents) and PyMOL Molecular Graphic Systems (Schrödinger, LLC) softwares. VSV infection. 293T cells were seeded in 12-well plate and were transfected 24h later with 800 ng of SAMD9L-encoding plasmid, ISG20-encoding plasmid  21  as a positive control for VSV restriction, or an empty plasmid as a negative control. Twenty-four hours post-transfection, cells were infected with VSV-GFP 55 at MOI 0. 3 and, 16 hours post-infection, the viral supernatant was collected for each condition and titered on new cells through the GFP reporter. Single-cell analysis was acquired with BD FACSCanto™ II Flow Cytometer (SFR BioSciences).
Plates were revealed using an NBT/BCiP solution (ThermoFisher Scientific) and foci were counted to determine the virus titers. Viral titers were expressed as Focus-Forming Units per milliliter (FFU/mL). Host and viral sequence analyses. SAMD9 and SAMD9L orthologs from mammals were retrieved from public online databases using NCBI BLASTn with the human sequences of SAMD9 and SAMD9L as queries. SLFN sequences were retrieved using NCBI. HIV and lentivirus sequences were retrieved using the HIV sequence database http://www. hiv. lanl. gov/. Translation alignments were performed using Muscle  57. Geneious (Biomatters) and ESPript 3. 0  58  were used for sequence displays.
Codon usage analysis. Codon usage preferences were measured using COUSIN (COdon Usage Similarity INdex)  39  through the online tool https://cousin. ird. fr/ with 18 492 transcripts from human 39 , 223 from VACV, 4 from VSV, 4 from MOPV, 3 from MLV, 9 from HIV-1 LAI, 9 from HIV-1 pWITO, and a total of 43 337 transcripts from different HIV and SIV strains obtained through the HIV sequence database http://www. hiv. lanl. gov/. Other softwares and statistical analyses. Sequencing analyses and representations were performed in Geneious (Biomatters). Quantification of images (Western-blot analyses) were performed using ImageJ https://www. imagej. org. Graphic representations and statistics were performed using GraphPad Prism 9 (except some with Excel from Microsoft Office). In Figures, data are represented as mean ± SD.
Acknowledgements We thank  Clarisse Berlioz-Torrent ,  Michael Emerman ,  Oliver Fregoso ,  Caroline Goujon ,  Molly OhAinle , and the LP2L members for helpful discussions on this project. We thank the  Etablissement Francais du Sang  for the leukopacks from blood donors and  Xuan-Nhi Nguyen  ( CIRI ) for some of the primary cell purifications. We thank  Jerome Bourret ,  Philippe Paget-Bailly  and  Ignacio Bravo  for discussion and support with the COUSIN analyses. We acknowledge the contribution of the  SFR BioSciences  ( UAR3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL) , PLATIM microscopy and ANIRA cytometry platforms, especially  Jacques Brocard  and  Véronique Barateau  for their help. We thank all the contributors of publicly available genome sequences and publicly available softwares.
Funding This work was funded by grants from the  French Research Agency on HIV and Emerging Infectious Diseases ANRS/MIE  (# ECTZ19143  and # ECTZ118944  to LE), as well as from the  ANR   LABEX ECOFECT  ( ANR-11-LABX-0048  of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency , to LE and LG), the  amfAR  ( Mathilde Krim Phase II Fellowship  # 109140-58-RKHF , to LE), the  "Fondation pour la Recherche Médicale " ( FRM "Projet Innovant " # ING20160435028  to LE), the  FINOVI  ("recently settled scientist" grant to LE), the  Sidaction  (to AC), and a  JORISS incubating grant  (to LE). LE and AC are supported by the  CNRS. LG is supported by the  Université Claude Bernard Lyon 1  and  Swedish Center of Advanced Study.
E-liquids The devices were filled with a standardised e-liquid  [30]  suitable for devices using low power inputs and contain 69% of propylene glycol (noted PG, CAS: 57-55-6, purity ≤99. 8%), 24. 5% of glycerol (noted VG, CAS: 56-81-5, purity ≤99. 5%), 1. 7% of ethanol (noted EtOH, CAS: 64-17-5, purity ≤96%), 4. 3% of purified water (noted H 2 O, CAS: 7732-18-5) and 1% of nicotine (CAS: 54-11-5). The physical properties of this liquid and its vapour--liquid equilibrium conditions are required to estimate the limits and are calculated using SimulisThermodynamics® from ProSim (see Table  2 ). This toolbox included in Excel provides a database of approximately 2000 compounds and allows the calculation of physical properties and their behaviours with temperature using proven models. The thermodynamic model used is the predictive model UNIFAC (UNIversal Functional Activity Coefficient)  [32]  that calculates the activity coefficient of a chemical molecule using a group contribution approach.
Identification of novel chemotypes with biological activity similar to a known active molecule is an important challenge in drug discovery called 'scaffold hopping'. Small-, medium-, and large-step scaffold hopping efforts may lead to increasing degrees of chemical structure novelty with respect to the parent compound. In the present paper, we focus on the problem of large-step scaffold hopping. We assembled a high quality and well characterized dataset of scaffold hopping examples comprising pairs of active molecules and including a variety of protein targets. This dataset was used to build a benchmark corresponding to the setting of real-life applications: one active molecule is known, and the second active is searched among a set of decoys chosen in a way to avoid statistical bias. This allowed us to evaluate the performance of computational methods for solving large-step scaffold hopping problems.
| Building a dataset of large-step scaffold hopping cases Our goal is to build a benchmark of large-step scaffold hopping cases that can be used by ligand-based computational methods. This allows to compare the performances of methods to solve scaffold hopping problems in the general case where the 3D structure of the considered protein is unknown. Nevertheless, we built this benchmark from examples extracted from the PDBbind database to ensure that the selected pairs of molecules are 'true' large-step scaffold hopping cases, i. e. , highly dissimilar compounds that share similar binding modes with the same protein, as identified by the same UNI-PROT ID. Indeed, for example, there would not be any rationale to relate two inhibitors of an enzyme binding to two distinct and distant binding sites, and such 'false' cases must not be present in the benchmark.
Note that the molecules binding modes are only used to select 'true' scaffold hopping cases when building the benchmark. They are not further used or provided to the considered computational methods, in order to remain in a ligand-based framework. Identification of such examples is not straightforward: some examples presented below show that it is not possible to use only one criterion based on a single molecular similarity measure, in order to build a reliable dataset where 'false' large-step cases and 'false' scaffold hopping cases are not present. The next subsections present the subsequent steps that are used, and more details are given in Materials and Methods.
| Filtering the PDBbind database To identify scaffold hopping cases, we need to search for pairs of highly different molecules that bind to the same protein pocket with similar binding modes, because molecules that would present totally different binding modes in the same pocket, or bind to different pockets of the protein, do not meet the definition of scaffold hopping. To enable the selection of such pairs, we use the PDBbind database  [16]  that contains 17. 652 PDB files (2019) of 3D crystallographic structures of protein-ligand complexes. We only keep structures with a resolution below 2:8 Å, to ensure that the binding modes of the ligands can be analysed with confidence. Second, as some compounds can be crystallized by soaking experiments even with unspecific affinities in the millimolar range, we remove all complexes with affinity above 10 μM.
| Discarding redundant pairs We observe that for some of the 79 proteins, the selected pairs are strongly redundant and represent only slightly different examples of scaffold hopping cases: they involve two molecules that belong to the same chemical series (for instance, they differ by the addition of a small group not involved in the binding). A compelling example is given in Figure  6. To avoid redundancy in our dataset, which may lead to bias for performance evaluation of computational methods, we remove pairs in which both ligands are similar to both ligands of another pair, using a threshold of 0. 5 on both their molecular Morgan similarities as detailed in Material and Methods, which finally leads to 178 pairs.
| Resulting large-step scaffold hopping dataset The global selection flowchart is shown in Figure  7. Overall, 178 large-step scaffold hopping cases of drug-like pairs binding to 79 different proteins are selected. On average, each protein is involved in 2. 3 large-step scaffold hopping cases in the dataset. For the most represented protein, cell division protein kinase 2, 10 cases are selected. The most represented family of proteins is the kinases family, with 61 pairs involving 21 different kinases. This can be explained by the fact that kinases belong to a highly studied family of proteins, with many therapeutic targets against which many drug design projects have been devoted  [31]. However, the dataset still contains significant protein diversity, since the 79 proteins belong to 35 different super-families of the SCOP protein family's hierarchy database  [32].
On average, each super-family is involved in 5. 1 scaffold hops. Pairwise sequence identities between proteins have been computed using the Needleman-Wunsch algorithm, which shows that they share modest similarity: on average, proteins have pairwise sequence identities of 8 %, and only 12 pairs (over a total of 3081 pairs of proteins) display identities above 30 %. Each selection step involves selection criteria with threshold values. The above sections show that one must be careful to avoid 'false' large-step hops, or 'false' scaffold hopping cases, which has scarcely been discussed in previous benchmark studies. In the present work, the thresholds are chosen arbitrarily and somewhat stringently, to build a highly reliable dataset, as judged by visual analysis of the selected pairs. Of course, these thresholds can be changed, as detailed in the Discussion F I G U R E 7 Flowchart describing the successive filters applied to identify large-step scaffold hopping cases.
| Choice of decoy molecules As illustrated in Figure  9 , our benchmark consists in a dataset of pairs of molecules representing large-step scaffold hopping cases for the associated protein, and their corresponding decoy molecules. Hence, for each of the 178 selected pairs, a set of decoys needs to be defined. In fact, all studies devoted to benchmarking virtual screening methods require the selection of decoy compounds, which is not an easy problem as we want to avoid statistical bias with respect to the active molecules  [33]. In particular, when decoys stand in regions of chemical space that are very distant from the two active molecules, the resulting benchmark may suffer from 'analogous bias'  [34] , and the success rate may be artificially overestimated. In addition, since we also want to mimic real-life applications, the decoys must be realistic scaffold hopping candidates that, in practice, would be searched among molecules sharing some physicochemical characteristics with the known active.
It has been shown that random selection of decoys from large chemical databases does not prevent the occurrence of statistical bias  [33]. Therefore, as detailed in Materials and Methods, decoys are picked in the ZINC database  [35] , among molecules that have similar physicochemical and complexity properties  [36]  with respect to the two molecules of their corresponding pair of actives. More precisely, for each pair, the decoys are selected in such a way that they are as distant to both molecules of the pair, as these two molecules are distant from each other, according to the Murcko-based and molecular Morgan similarities. For 144 pairs out of the 178 pairs selected in the previous sections, we are able to find 499 decoy molecules that satisfy these criteria. Note that we cannot rule out the possibility of a few false negatives  [37, 38] , because we may have accidentally picked.