dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016). Disease Models & Mechanisms • DMM • Accepted manuscript (Brca2, In sum, BMC differentiation correlated with the decreased expression of 76 genes that may impact on telomere biology (Fig. 1B , Table  S1 ). Consistent with the notion that BMC differentiation strongly correlates with p53 activation in this system, 72 of these 76 genes have negative p53 expression score(s) in the Target gene regulation (TGR) database  (Fischer et al. , 2022a) , which indicates that they were downregulated upon p53 activation in most experiments carried out in mouse and/or human cells (Fig. 1B , Table  S1 ). We previously showed that p53 activation leads to an increased binding of the E2F4 repressor at the promoter of 4 of these 72 telomere-related genes (Brca2, Fancd2, Rad51, Rtel1)  (Jaber et al. , 2016) , which contributed to provide evidence that p53-mediated gene repression often occurs indirectly, through the recruitment of the E2F4-containing complex DREAM, often close to transcription start sites.
Disease Models & Mechanisms • DMM • Accepted manuscript S3 ). Average ChIP-seq binding scores appeared slightly higher in human cells, particularly with antibodies against LIN9 (average score of 638 for 59 human genes, compared to 496 for 36 mouse genes). This might suggest that antibodies against LIN9 were more efficient to precipitate the human LIN9 protein, so that the number of murine genes downregulated by p53-DREAM was under-estimated due to technical difficulties. In sum, a total of 61 telomere-related genes were detected in ChIP assays with antibodies against E2F4 and LIN9 in at least one species (Fig. 1C ), strengthening the notion that the p53-DREAM pathway plays a significant role in regulating telomere biology. We previously reported that p53 can also downregulate many genes of the Fanconi anemia DNA repair pathway, a pathway implicated in the repair of DNA interstrand cross-links  (Jaber et al.
, 2016). Accordingly, GOrilla revealed a significant enrichment for genes of the "interstrand cross-link repair" GO term among the genes downregulated in murine differentiated BMCs (Table  S4A ). We found 55 genes down-regulated upon BMC differentiation, encompassing genes mutated in Fanconi anemia, regulating the Fanconi DNA repair pathway and/or belonging to the GOrilla interstrand cross-link repair GO term, or to a recently proposed list of FA-related genes  (Wang et al. , 2021) , including 52 genes downregulated by p53 according to the TGR database (Fig. S2A , Table  S5 ). Out of these 52 genes, twelve are also known to impact on telomere biology  (Atm, Atr, Brca2, Dclre1b, Fancd2, Fen1, Hus1, Rad51, Rad51ap1, Rpa2, Telo2, Xrcc3). ChIP-seq experiments revealed E2F4 binding at 51, and LIN9 binding at 41 of the 52 genes (Fig.
5-fold upon tamoxifen withdrawal, a significant enrichment was observed for 28 GO terms related to ribosome biology, rRNA biogenesis and maturation, or RNA polymerase I (Table  S4B ). These 28 GO terms are partially overlapping and correspond to 168 different genes, of which  10 (Dkc1, Exosc10, Gar1, Gnl3l, Naf1, Nat10, Nhp2, Nop10, Prkdc, Shq1)  are also known to impact on telomere biology. Furthermore, we noticed 3 additional genes encoding subunits of RNA Polymerase I (Polr1d, Taf1c, Taf1d) that were downregulated upon BMC differentiation, raising the total of candidates to 171 genes, of which 162 are downregulated Disease Models & Mechanisms • DMM • Accepted manuscript by p53 according to the TGR database (Fig. S3 , Table  S8 ). E2F4 binding was found at 152, and LIN9 binding at 50 of the 162 murine genes, at sequences frequently overlapping transcription start sites (Fig.
Out of these 17 genes, 15 were reported to be downregulated upon p53 activation (Fig. S4 , Table  S11 ). Out of the 15 genes, 14 were bound by E2F4 and 6 were bound by LIN9 in murine cells (Fig. S4 , Table  S12 ) and 13 were bound by E2F4 and 12 by LIN9 in human cells (Fig. S4 , Table  S13 ). We also used the Human Phenotype Ontology website from the Jackson Laboratory (https://hpo. jax. org)  (Köhler et al. , 2021)  to search for genes associated with abnormalities of blood and blood-forming tissues (ontology term #HP:0001871), and found that, out of a list of 1322 genes, 336 candidates were downregulated at least 1. 5 times upon murine BMC differentiation, including 277 reported to be downregulated by p53 according to the TGR database (Fig. S5 , Table  S14 ). Out of these 277 genes, 243 were bound by E2F4 and 102 by LIN9, close to the transcription start site in most cases (Fig.
S5 , Table  S15 ). Out of the 277 human homologous genes, 245 were bound by E2F4 and 198 by LIN9 (Fig. S5 , Table  S16 ). Together, the differentiation of bone marrow cells correlated with the decreased expression of a total of 571 genes implicated in hematopoiesis, including 499 genes downregulated by p53 according to the TGR database (Table  S17A -B, see also Fig. 3C  for a summary of our approach). For 374 of these genes, E2F4 and LIN9 were found to bind at identical regions in at least one species (Table  S17C , Fig. 3C ). Furthermore, to focus on the best candidate p53-DREAM targets, we also considered the ChIP scores for E2F4 and LIN9 binding for each of the 374 genes. For each gene, we added the ChIP scores of E2F4 and LIN9 in both species, for a maximal value of 4000 (Table  S18 ).
Total ChIP scores ranged from 313 to 4000, and we noticed total ChIP scores of 656 and 720, respectively for Fbl and Dkc1, two genes reported to be directly repressed by p53 binding Disease Models & Mechanisms • DMM • Accepted manuscript  (Marcel et al. , 2013; Simeonova et al. , 2013)  and a score of 979 for Exosc5, a gene previously proposed to be regulated by DREAM  (Fischer et al. , 2016b). We thus considered the 269 genes with a total ChIP score ≥ 979 as the most likely candidate p53-DREAM targets (Tables  S17D, S18  and Fig. 3C ). To estimate the relevance of this list of 269 candidates, we analyzed the dataset GSE171697, which includes RNAseq data from hematopoietic stem cells of unirradiated p53 KO mice, unirradiated WT mice, or irradiated WT mice  (Tung et al. , 2021). We also analyzed GSE204924, with RNAseq data from splenic cells of irradiated p53 Δ24/-or p53 +/-mice  (Resnick-Silverman et al.
, 2023). The public data from this dataset, although incomplete, appeared interesting because p53 Δ24 is a mouse model prone to bone marrow failure  (Hamard et al. , 2013)  and the spleen is an hematopoietic organ in mice  (Iseki et al. , 2008). As expected, increased p53 activity correlated with an average increase in expression for 15 genes known to be transactivated by p53, with 13/15 genes upregulated at least 1. 5-fold (Table  S19 ). By contrast, only 56/269 candidate p53-DREAM targets genes appeared upregulated in cells with increased p53 activity (Table  S19 ). These 56 genes were considered poor candidate p53-DREAM targets and removed from further analyses, leading to a list of 213 candidate p53-DREAM targets related to blood abnormalities (Tables S17E, S19 and Fig. 3C ).
A downregulation of gene expression of at least 1. 5-fold upon BMC differentiation was found for 64 of these candidates, including 57 reported to be Disease Models & Mechanisms • DMM • Accepted manuscript downregulated upon p53 activation according to the TGR database (Fig. 1D , Table  S20 ). Out of the 57 genes, 55 were bound by E2F4 and 36 by LIN9, within regions overlapping the transcription start site in most cases (Fig. 1E , Table  S21 ). Out of the 57 human homologs, all were bound by E2F4 and 49 by LIN9 (Fig. 1E , Table  S22 ). We next searched the Human Phenotype Ontology website from Jackson Laboratory for genes associated with microcephaly or cerebellar hypoplasia (ontology terms #HP:0000252 and HP:0007360) and found that, out of a list of 1430 genes, 474 candidates were downregulated at least 1.
5 times upon murine BMC differentiation, including 404 reported to be downregulated upon p53 activation (Fig. S6 , Table  S23 ). Out of these 404 genes, 354 were bound by E2F4 and 153 by LIN9, in regions overlapping transcription start sites in most cases (Fig. S6 , Table  S24 ). Out of the 404 human homologous genes, 371 were bound by E2F4 and 292 by LIN9 (Fig. S6 , Table  S25 ). In sum, the differentiation of bone marrow cells correlated with the decreased expression of 478 genes implicated in microcephaly or cerebellar hypoplasia, including 408 downregulated upon p53 activation according to the TGR database (Table  S26A -B and Fig. 3C ). For 303 of these genes, E2F4 and LIN9 were found to bind at identical regions in at least one species (Table  S26C , Fig. 3C ). Furthermore, total ChIP scores ≥ 979 were found for 226 of the 303 genes, which appeared as better candidate p53-DREAM targets (Tables  S26D, S27 , Fig.
3C ). To estimate the relevance of this list of 226 candidates, we analyzed datasets GSE78711 and GSE80434, containing RNAseq data from human cortical neural progenitors infected by the Zika virus (ZIKV) or mock-infected, because ZIKV was shown to cause p53 activation in cortical neural progenitors and microcephaly  (Tang et al. , 2016; Zhang et al. , 2016). Accordingly, most genes (12/16) known to be transactivated by p53 were upregulated in ZIKV-infected cells (Table  S28 ). By contrast, only 64/226 candidate p53-DREAM targets genes appeared upregulated in ZIKV-infected cells (Table  S28 ). These 64 genes were considered poor candidate p53-DREAM targets and removed from further analyses, leading to a list of 162 candidate p53-DREAM targets related to brain abnormalities (Tables  S26E, S28  and Fig.
Disease Models & Mechanisms • DMM • Accepted manuscript with ChIP peaks for DREAM subunits and transcription start sites (TSS), and most DBS tested experimentally were found to affect gene expression in luciferase assays, suggesting reliable DBS predictions. The Target gene regulation (TGR) database of p53 and cell-cycle genes was reported to include putative DREAM binding sites for human genes, based on separate genome-wide searches for 7 bp-long E2F or 5 bp-long CHR motifs  (Fischer et al. , 2022a). We analyzed the predictions of the TGR database for the 151 genes for which we had found putative bipartite DBS. A total of 342 E2F binding sites were reported at the promoters of these genes, but only 64 CHR motifs. The similarities between the predicted E2F or CHR sites from the TGR database and our predicted bipartite DBS appeared rather limited: only 14/342 E2F sites overlapped at least partially with the GC-rich motif of our bipartite DBS, while 27/64 CHR motifs from the TGR database exhibited a partial overlap with the AT-rich motif.
Importantly, most E2F and CHR sites from the TGR database mapped close to E2F4 and LIN9 ChIP peaks, but only 16% of E2Fs (54/342), and 33% of CHRs (21/64) mapped precisely at the level of these peaks (Fig. S11 ), compared to 55% (83/151) of our bipartite DBS (Fig. 3A ). Thus, at least for genes with bipartite DREAM binding sites, our method relying on PFM22 appeared to provide more reliable predictions of DREAM binding than the E2F and CHR sites reported separately in the TGR database. Importantly however, predictions of the TGR database may include genes regulated by a single E2F or a single CHR that would most likely remain undetected with PFM22, suggesting that both approaches provide complementary results. Of note, we previously used Consite (http://consite. genereg. net)  (Sandelin et al.
, 2020). Our analysis suggests that many targets of the p53-DREAM pathway are associated with syndromes of abnormal hematopoiesis or brain development. To get a more precise evaluation of this association, we searched for genetic disorders that might be caused by the mutation of any the 151 candidate p53-DREAM targets for which putative DBS were identified. According to OMIM, the online catalog of human genes and genetic disorders (https://www. omim. org), 106/151 genes were mutated in a hematological or neurological disorder. Among these, 25 were mutated in Disease Models & Mechanisms • DMM • Accepted manuscript syndromes characterized by anemia, lymphopenia, neutropenia or thrombocytopenia and 77 in syndromes with microcephaly, cerebellar hypoplasia or hypoplasia of cerebellar vermis, including 13 associated with both types of symptoms (Tables  3  and S36 ).
Transcriptome data comparisons We analyzed the gene expression data from Hoxa9-ER expressing hematopoietic stem and progenitor cells grown in the presence of tamoxifen, or in differentiated cells 5 days after tamoxifen withdrawal, a microarray study relying on 45,101 probes corresponding to 20,627 genes (Gene Expression Omnibus #GSE21299)  (Muntean et al. , 2010). For each probe, we calculated the inverse of Log2 from robust multi-average values. The obtained average (from triplicates) for cells with tamoxifen was given a value of 1, and the ratios before and after tamoxifen withdrawal were calculated. For each gene, we took the probe leading to the highest repression ratio into account and selected those downregulated at least 1. 5-fold upon tamoxifen withdrawal. Among those genes, we identified targets downregulated by human and/or mouse p53 by consulting p53 regulation scores in the Target gene regulation database (www.
RNAseq data analyses To screen for the most relevant candidate p53-DREAM targets, we analyzed publicly available datasets GSE171697 and GSE204924 for blood ontology-related genes, and datasets GSE78711 and GSE80434 for brain ontology-related genes. In addition, we analyzed the dataset GSE121720, containing RNAseq data from glioblastoma cells isolated from patient specimens. This dataset contains 92 samples, which were ranked according to BRD8 expression levels, and the top and bottom 25% samples were assigned as the BRD8 high and BRD8 low groups, as previously described  (Sun et al. , 2023; Wu et al. , 2020). Data for the 77 p53-DREAM targets associated with microcephaly or cerebellar hypoplasia (from Table  3 ) were then retrieved from the dataset and analyzed.
Search for putative DREAM binding sites To search for putative DREAM binding sites, we used PWMScan (https://ccg. epfl. ch/pwmtools/pwmscan. php  (Ambrosini et al. , 2018) ) with a custom positional frequency matrix from 10, 21 or 22 murine functional DBS (for details see Fig. S7 ), on both the mouse (mm10) and human (hg38) genomes, with a P value threshold of 10 -3 for cut-off. The putative DREAM binding sites identified were then analyzed for sequence conservation between mouse and human genomes and classified according to PFM score and number of mismatches between the two species at positions 2-6 (for CDE or E2F) and 11-16 (for CHR or CLE) of the DREAM binding sites. For 151 genes, the identified putative DBS, as well as putative E2F or CHR sites reported in the Transcriptional regulation database, were mapped relative to ChIP peaks (or transcriptional start sites) by using the Integrative Genomics Viewer (igv version 2.
GO term Genes are ordered as in Figure  S4. ChIP scores and positions are from ChIP-Atlas, H. sapiens hg38. Click here to download Table  S13  Table  S14. Genes associated with abnormalities in blood and bloodforming tissues and downregulated upon HSC differentiation, and their potential regulation by p53. Genes are associated with abnormalities in blood and blood-forming tissues according to Human Phenotype Ontology (#HP:0001871). Expression values from triplicates were retrieved from dataset #GSE21299. Genes are listed according to decreasing repression fold. Scores from the Target gene regulation (TGR) database indicate p53-regulated genes, with negative p53 TGR scores for repressed genes. Genes with a negative p53 TGR score in at least one species are highlighted in bold type. Click here to download Genes with a negative p53 TGR score in at least one species (Table  S14 ) were analyzed, and ordered as in Figure  S5.
ChIP scores and positions are from ChIP-Atlas, M. musculus, mm10. Click here to download Table  S15  Table  S16. E2F4 and/or LIN9 binding relative to the TSS of human homologs to the murine genes associated with abnormalities in blood and blood-forming tissues listed in Table  S15. Genes are ordered as in Figure  S5. ChIP scores and positions are from ChIP-Atlas, H. sapiens hg38. Click here to download Table  S16  Table  S17. A summary of our approach to select blood ontology-related candidate p53-DREAM targets. see text for details. Click here to download Table S17  Expression values from triplicates were retrieved from dataset #GSE21299. Genes are listed according to decreasing repression fold. Scores from the Target gene regulation (TGR) database indicate p53-regulated genes, with negative p53 TGR scores for repressed genes.
Disease Models & Mechanisms • Supplementary information Table  S35. Expression of 151 p53-DREAM targets in Lin37 KO and Lin37rescued murine cells. For the 151 genes with putative DREAM binding sites (see Table  2 ), we extracted RNAseq data from dataset GSE97716. The 51 genes reported by  Mages et al. to be differentially expressed upon Lin37 reintroduction are noted in bold. Click here to download Table  S35  Table  S36. Candidate p53-DREAM targets mutated in blood or brain genetic disorders. A,L,N,T: anemia, lymphopenia, neutropenia or thrombocytopenia ; OBA : other blood abnormalities; M, CH, HCV: microcephaly, cerebellar hypoplasia or hypoplasia of the cerebellar vermis; ONS: other neuropathological symptoms Click here to download Table  S36  Table  S37. The expression of p53-DREAM targets associated with neurological disorders is increased in glioblastoma cells with high BRD8 levels.
RNAseq data for BRD8, five genes transactivated by p53 (CDKN1A, BAX, GADD45A, MDM2, PLK3) and the 77 p53-DREAM targets associated with microcephaly or cerebellar hypoplasia (listed in Table  3 ) were extracted from dataset GSE121720. The 23 glioblastoma samples with low expression levels of BRD8 (BRD8Low) are listed in bold type, the 23 samples with high BRD8 expression levels (BRD8High) are listed in bold italic type. Click here to download Table S37.
g. if it is a survey, review, white paper, etc), a brief description of the methodology used and the assets involved, and the number of references and collected threats. It is worth noticing that there is no standard and commonly accepted way to produce a threat model, as a consequence, some papers are scientific surveys very detailed, while other papers, instead simply collect threats in an unstructured way. It must be noted that the following list of papers is a subset of the systematic Literature Review (SLR) we conducted to build up our threat catalogue, described in detail in the following section VI. We selected for this comparison the papers that contain surveys summarizing clearly the evolution of the 5G security challenges. it is important to point out that the aim of our work is more specific: obtain the data needed to build a catalogue containing threats affecting 5G components.
III. BACKGROUND: GENERATION OF FINE-GRAINED THREAT MODELS The idea of generating fine-grained threat models from a graph-based model and a threat catalogue relies on some preliminary works  [8] ,  [21] -  [23]  that use some static, but well-known sources, like OWASP top threats and the most relevant scientific papers. A part of the catalogue was built in the context of the MUSA H2020 project. The main limit of the approaches proposed above was related to the difficulties in build, maintaining and validating the threat catalogue. The paper  [24]  describes in detail the process for generation of the threat model generation. in the following, we briefly summarize the catalogue data model and the generation algorithm, in order to improve readability, but we invite the interested reader to find the details in the referred paper.
The Systematic Threat Search will end (similarly to the systematic literature review) with a data extraction phase, that will propose a list of threats and their possible taxonomies. The Threat Data Analysis will collect all the data extracted by the Systematic Threat Search and describe the threats according to our model, identifying the compromised asset, the threat behaviours the possible threat agents and all the other information that we store in our catalogue (as described in detail in section III. In this phase, we will solve the possible threat duplicates and other incoherence that may appear when comparing the different sources of data. The produced threats will enrich our Threat Catalogue. As a final result, we will obtain: • The extension of our modeling technique for the target domain • the list of threats applicable to the model (The Threat Catalogue) needed to generate fine-grained threat models.
The customer uses a device (User Equipment) to connect to the network through an Access Network (AN) to access the Data Network (typical external networks, e. g. Internet). Note that the AN can be 5G (NG-RAN), but may even be an older technology enabling interaction among new infrastructure and legacy systems. The Core network manages the system resources through the NFs illustrated in the figure and briefly listed above, enabling the management of the full network infrastructure. • UPF, User Plane Function, handling the user data For what regards the security functionality of the network, the 5G Core includes a Network Exposure Function (NEF), Authentication Server Function (AUSF) and Security Anchor Functionality (SEAF). For a detailed analysis of the behaviours of each function and their interaction, we invite the interested reader to check the already cited standard TS 23.
As a matter of fact, such protocols, defined in the 5G specifications and publicly available, (all references can be found in TS 23. 501  [31] ) are REST-based protocols, based in HTTP/HTTP2 having SCTP as the transport protocol. Moreover, the protocols offered by the NFs reuse/adaptation of many of the existing application protocols. In the following, we briefly summarize a list of protocols involved in 5G, which should be considered later for threat modeling. • NAS: the non-access stratum (NAS) is the highest stratum of the control plane between UE and MME at the radio interface. The main functions of the protocols that are part of the NAS are the support of mobility of the user equipment (UE) and the support of session management procedures to establish and maintain IP connectivity between the UE and a packet data network gateway (PDN GW).
We noticed (through repetitive queries) that the keyword 5G (case insensitive) was enough to identify all papers that address this domain. In other cases, a more complex selection could be necessary. We also needed eligibility criteria to select the relevant works that can answer the research questions. All the chosen criteria are shown in the tables 5 and 6. The papers from which data are obtained are the ones that contain threat models or classifications related to a single part or the overall 5G architecture. Also, white papers, reviews and surveys have been used to obtain threats. Once the papers have been selected using the inclusion/exclusion criteria, the form fields used for the Data Extraction are described in the table  7. By collecting these data, we expect to get both an exhaustive overview of threat modeling approaches used in the literature and a complete threat classification related to 5G architecture.
PrP-Halo labeling To label cell surface PrP-Halo, cells were incubated for 15 min at 37 C with the HaloTag Alexa Fluor 488 cellimpermeant ligand (Promega G1001) diluted 1:1000 in the culture medium, then rinsed three times with PBS. PrP-Halo fluorescence was analyzed by structured illumination microscopy, or in-gel after SDS-PAGE, using a Chemidoc Imager MP (Bio-Rad), and quantitative densitometry of protein bands with ImageLab 6. 0 software (Bio-Rad).