dataesr/scientific-paragraphs-categorization huggingface dataset:
Results We identified 413 relevant citations, of which 45 fulfilled the inclusion criteria (Figure  2 ; Table  S2 ). Of the 45 data sources that were identified by the initial searches or recommended by the Literature Review Group, 17 articles were published in peerreviewed journals; the remaining sources were published as InVS publications (n = 16), Institut Pasteur reports (n = 8), conference presentations (n = 3) or French Ministry of Health reports (n = 1). The study designs varied, but the majority (n = 22) were surveillance reports; the remaining data sources were outbreak reports (n = 2), prospective studies (n = 9), retrospective studies (n = 6), and 'other' (n = 6) (Table  S2 ). A narrative synthesis of our findings is presented. From the 45 publications, data were retrieved for the five territories as follows: 14 publications for French Guiana; 11 publications for Martinique; seven publications for Guadeloupe; six publications for Saint Martin; five publications for Saint Barthelemy; and 13 publications for French West Indies and French Guiana.
Epidemiology of dengue in Martinique and Guadeloupe Epidemics. The timing and duration of the epidemics during the review period in Guadeloupe  (2005-2006, 2007-2008 and 2010)  were similar to those in Martinique  (2001-2002, 2005-2006, 2007-2008  and 2010) (Table  2 ). The magnitude of the 2005-2006 epidemics was similar between Martinique and Guadeloupe, with an incidence rate of 3700 and 2000 suspected cases per 100,000 population, respectively  [13, 30, 42]. The magnitude of the 2007-2008 epidemic was similar in Guadeloupe and in Martinique, with 4744 and 4524-4526 suspected cases per 100,000 population, respectively  [13, 17, 36, 38, [42] [43] [44]. The largest of the epidemics in both territories were in 2010, first starting in Guadeloupe in November 2009, lasting 47 weeks until October LILACS, Latin American and Caribbean Health Science Literature; PRISMA, preferred reporting items of systematic reviews and meta-analyses; WHOLIS, World Health Organization Library database; SciELO, Scientific Electronic Library Online.
All references identified in the on-line database searches were assigned a unique identification number. After a review of the titles and abstracts duplicates and articles were removed according to the following inclusion/exclusion criteria. Studies published in French or English between 1 January 2000 and 29 March 2012 were included. No limits by sex, age or ethnicity of study participants or by study type were imposed. Single-case reports (except reports of deaths), incomplete surveillance reports and studies that only reported data for the period before 1 January 2000 were excluded. As duplicate publication of data (e. g. , in metaanalyses and other reviews) could lead to oversampling and overestimates of the incidence of dengue, editorials involving previously published peerreviewed data were also excluded.
Gaps in epidemiological knowledge There were substantial gaps in epidemiological knowledge regarding DENVs in the region. In particular, information regarding DENV serotypes was limited during inter-epidemic periods, with the exception of French Guiana where the National Reference Center of arboviruses of the Pasteur Institute is located. Additionally, the available data were not always representative of the population as a whole. During epidemics, most of the analysed samples were taken from hospitalized cases, so circulating serotypes in non-severe cases were not well documented. Moreover, data regarding DENV genotypes circulating in the FTA were not routinely available. In addition, the impact of population movements (e. g. , due to tourism and immigration) on the circulating serotypes and local serotype evolution in these highly touristic areas was not available, but would be useful to understand and document the epidemiological situation.
Figure2. Results of the literature search and evaluation of identified studies according to PRISMA. EMBASE, Excerpta Medica Database; LILACS, Latin American and Caribbean Health Science Literature; PRISMA, preferred reporting items of systematic reviews and meta-analyses; WHOLIS, World Health Organization Library database; SciELO, Scientific Electronic Library Online. All references identified in the on-line database searches were assigned a unique identification number. After a review of the titles and abstracts duplicates and articles were removed according to the following inclusion/exclusion criteria. Studies published in French or English between 1 January 2000 and 29 March 2012 were included. No limits by sex, age or ethnicity of study participants or by study type were imposed. Single-case reports (except reports of deaths), incomplete surveillance reports and studies that only reported data for the period before 1 January 2000 were excluded.
Acknowledgments The authors take full responsibility for the content of this contribution and thank  Jo Mulcahy  ( Communigen Ltd , supported by  Sanofi Pasteur)  for editorial and administrative assistance with preparation of the manuscript. The authors also thank  Gee Marsh  ( Sanofi Pasteur ) for provision of critical comments and suggestions on the drafts and  Anne Sainfort  for her contribution to the documentation of historical dengue information in the  French West Indies and French Guiana.
MEF immortalisation is thus a simple and powerful strategy for modelling cancer mutation landscapes that facilitates the interpretation of human tumour genome-wide sequencing data. H igh-throughput sequencing has shown that cancer genomes are riddled with somatic alterations, with numerous tumour types harbouring hundreds to thousands of mutations. Bioinformatic analyses of this massive catalogue in search of recurring mutations have substantially contributed to the identification of most of the genes functionally impaired in cancer development  1. The genome-wide human cancer sequencing data also provide a powerful resource for investigating the nature of mutagenic insults that give rise to mutations in human population  2, 3. However, what is lacking in order to make optimal use of this resource for such a purpose is a suitable database of experimentally induced mutations to test inferences that come from inspecting human mutation patterns.
In the present study, we expanded this approach by assessing exome-wide mutation patterns in the Hupki MEF immortalisation assay (Supplementary Fig. 1 ). We sequenced the exome of immortalised MEF lines established from primary cultures exposed to wellknown carcinogens and compared the mutation profiles obtained in these assays to those observed in genome-wide data from human tumours with related aetiologies. Mutation signatures derived from these assays were also compared to currently known signatures in human cancers  2. Since the MEF in vitro immortalisation process has parallels with the conversion of normal cells to tumour cells in vivo, we also investigated alterations in specific driver genes that could provide mechanistic clues to molecular events governing senescence bypass and immortalisation.
Results Genome-wide mutation spectra from immortalised MEF cell lines. Genomic DNA isolated from primary MEFs and from immortalised cell lines derived from MEF cultures exposed to aristolochic acid (AA), ultraviolet light subclass C (UVC), the alkylating agent N-methyl-N'-nitro-N-nitrosoguanidine (MNNG), the tobacco mutagen benzo(a)pyrene (BaP), or unexposed cultures (see Supplementary Table  1 ), were subjected to genome-wide mutation profiling by whole-exome sequencing (WES) (see Supplementary Fig. 1  for assay overview and Methods for WES data processing and analysis). Two cell lines per exposure category were investigated. As shown in Fig. 1 , the patterns of mutations found in the MEF cell lines were in marked concordance with those observed in human tumours with aetiologies related to the mutagens tested, and were as expected from previous knowledge on the mutagenic properties of these particular exposures.
T), as in the human lung tumour dataset (Fig. 1h ). In cell lines from MNNG cultures, C. T transitions with a C or T in 39 and any base in 59 were the most frequent (corresponding to 59-(G/A)GN-39 for the complementary G. A mutations), observed also in recurrent glioblastomas of temozolomide-treated patients (Fig. 1i ). These mutations occurred mainly outside CpG sites as expected. In the cell lines derived from UVC treated MEF cultures, C. T changes within a 59-(C/T)CN-39 motif were the major events as seen in human skin SCC (Fig. 1j ). This context is expected from the published literature on UV mutagenesis, which describes the highly characteristic alterations at pyrimidine dimers induced by UV exposure. Interestingly, the frequent C. G mutations found in the spontaneous lines showed a preferred sequence context for 59-GCC-39 a signature that was also present, although much less prominently, in most of the other cell lines (Fig.
T transitions in these latter cell lines. Two types of statistical analyses were then applied to these data (Fig. 2 ). Firstly, principal component analysis (PCA) was performed to assess whether global mutation patterns obtained in the MEF immortalisation assays can distinguish between cell lines obtained from different treatments/conditions. Using percent frequency values of the six mutation types in their triplet sequence context (amounting to a total of 96 variables), the two first components were able to discriminate the replicate cell lines according to each specific treatment condition (Fig. 2a ). When including the human cancer datasets in the exposure model, we observed a good concordance with the mouse datasets for most conditions, with the exception of the UVC treatment which showed a broader confidence interval (Fig.
2b ). Secondly, the method used by Alexandrov et al. 24  , to extract signatures was adapted and applied to the 14 cell line data (see Methods). Although this method is optimized for large datasets, it could identify six signatures that corresponded to the six experimental conditions (Supplementary Fig. 2 ) and were concordant with the mutation patterns shown in Fig. 1. The comparison of the MEF experimental signatures with the 27 human-cancer derived signatures reported by Alexandrov et al. 2  , showed high similarity between the MNNG signature and Signature 11 (temolozomide), similarity between the BaP signature and Signature 4 (smoking), and between AID signature and Signature 19 (not identified) (Fig. 2c ). No similarity was found for the AA signature (patients with AA-associated tumours were not analysed by Alexandrov et al.
1  , as well as genes encoding regulators of the epigenome that have been described as a newly emerging class of cancer driver genes  1, 25, 26. Non-synonymous and truncating mutations found in these selected driver genes are detailed in Supplementary Dataset 1 and graphically summarized in Supplementary Dataset 2. A number of genes in these functional classes were found altered by mutations characteristic of the exposure that cells underwent prior to immortalisation. Although most genes were mutated only in one line, the Ep400, Dnmt1, Kdm6b, Kmt2d, Arid1b and Arid2 genes were mutated in at least two lines. Ep400 and Kmt2d in particular were mutated in four cell lines. Ep400, a regulator of cellular senescence within the p53-p21 axis  27  , was affected by a truncating mutation in one line, and Kmt2d carried three mutations in important functional domains.
The scope of overlap between mutation patterns in human datasets and immortalised MEF lines includes: (a) the global distribution of mutation types, (b) the accumulation of mutations on the non-transcribed strand (strand bias) for treatments with carcinogens known to elicit transcription-coupled DNA repair, and (c) the sequence context of the dominant mutation type. Thus, using four carcinogens with well-known mutagenic properties, the predominant mutation signatures we found with this model for the four tested carcinogens were the ones expected for these mutagenic agents. Although we analysed only two cell lines for each carcinogen, the expected signatures were evident in single cell lines and were highly reproducible between the two cell lines. The human tumour datasets used for comparison with our in vitro data were selected from publicly available data and our selection was based on whether the suspected aetiologies of the tumour sets were linked to the carcinogen tested in MEFs.
The most striking matching condition was the AA treatment. In both human and in vitro MEF data, over 50% of mutations were A. T transversions, with a significant strand bias of 251 and a sequence context dominated by 59-CAG-39. The aetiology of the tumours included in the human set has been clearly associated with the AA exposure  14. Since AA is a potent carcinogen that mainly causes A. T transversions, enrichment of these somatic mutations in exposed individuals is likely to reflect the insult of AA exposure. The AA signature has not been found in any other cancer type so far  32. In the case of the in vitro WES data from cell lines arising from MNNG-exposed cultures, the human set chosen for comparison consisted of patients treated with the drug temozolomide, which, like MNNG, is an alkylating agent.
The global mutation type distribution was strikingly similar between the mouse and human data and very distinct from primary tumours of the same type but not exposed to temozolomide (Supplementary Fig. 3 ). The MNNG signature was  in vitro data from BaP exposure with respect to the less prominent mutation types. Tobacco smoke contains a highly complex mixture of carcinogens. Nevertheless, the BaP signature derived from the in vitro assay exhibited similarity with the human tumour-derived smoking signature reported previously 2 , suggesting that BaP and possibly other smoke components that have similar mutagenic properties constitute one of the main carcinogenic insults responsible for the smoking signature observed in human tumours. The dataset from tumours associated with UV exposure was the most distant from the signature obtained in vitro, although the expected C.
T mutations within a 59-(C/T)CN-39 context were prominent in both sets. There are several possible explanations, such as the technical aspects of the experimental procedure in vitro, or the mutagenic activities of sunlight compared to UVC alone. These results are reflected in the principal component analysis that showed the closest relationship between human and MEF data for AA and BaP and a more distant relationship for UVC. In addition to exogenous exposures, the spontaneous decay of DNA is a well-known cause of the human mutation load  33  and the deregulation of endogenous cellular enzymes that accelerate the accumulation of sequence changes is becoming of increasing interest to cancer biologists. It is a considerable challenge, however, to determine the relative contributions of different DNA metabolism pathways to genetic alterations observed in human cancers, and to understand the factors that may result in the deregulation of normal processes governing DNA integrity.
Further investigation of the origin of these C. G substitutions in the MEF in vitro assay is warranted. Although the number of cell lines analysed in the present study is limited, we identified several recurrently mutated genes among oncogenes and tumour suppressor genes classified as cancer drivers, or regulators of the epigenome, an emerging new class of potential driver genes. We note, however, that most of these mutations are likely to be passenger events occurring in the MEF immortalisation/ transformation process, analogous to observations in human cancers. Interestingly, while mutations in all categories of genes but histone genes were observed in the carcinogen-exposed lines, the HxAID-Tg cell lines accumulated mutations mainly in histone genes (Supplementary Datasets 1 and 2). A study to explore the reasons for this observation will require larger numbers of immortalised cell lines both with and without the AID transgene.
The analyses of indels might also provide a more complete picture of mutations in tumour suppressor genes as this type of cancer gene is more often altered by indels. The present study is limited to a small number of cell lines, and these have acquired typical human tumour TP53 mutations during immortalisation. The p53 gene mutation is the most common specific alteration known to drive senescence bypass and immortalisation of MEFs  42, 43. It will be interesting to investigate to what extent the p53 status influences global mutation patterns and the subset of mutated driver genes by comparing WES data from cell lines retaining the wild-type p53 gene sequence with cell lines that have acquired p53 mutations typical of human tumours. In summary, the present study demonstrates the potential of the MEF immortalisation assay to reveal mutation signatures of human carcinogens.
Libraries were then subjected to exome enrichment using SureSelect XT Mouse All Exon Kit (Agilent Technologies, Wilmington, DE USA) following manufacturer's instructions. Enrichment was verified by qPCR and the quality, quantity and fragment size distribution of DNA determined by an Agilent Bioanalyzer or Tapestation. The libraries were sequenced in paired-end 100 nucleotide (nt) reads using the Illumina HiSeq2500 platform according to manufacturer's protocols. MEF whole-exome data processing. All FASTQ files were analysed with FastQC to check sample homogeneity and quality. The FASTQ sequences were next aligned to the mm9 mouse reference genome with Burrows-Wheeler Aligner (BWA, version 0. 7. 5a) and the resulting SAM file was sorted and compressed in BAM format using Picard SortSam (version 1.
The recalibrated BAM files were used to call variants with MuTect software (version 1. 1. 4) using default parameters (including reads quality. 20 and calls made only if the position has at least 14 reads in the tumour sample and at least 8 reads in the normal sample). As MuTect is tuned to perform normal/tumour comparison, primary cell cultures were used as ''normal'' samples and immortalised cell lines as ''tumour'' samples. Each immortalised cell line was compared to two primary MEF cultures and only the overlapping calls were taken into consideration to maximize the chance of robust variant calls and to exclude potential polymorphisms. Human genome-wide sequencing datasets. Publicly available somatic mutation data obtained from whole-genome or whole-exome sequencing of human tumours were retrieved from the Catalogue Of Somatic Mutations In Cancer (COSMIC) database or original papers (selecting only SBS): a set of 14,715 substitutions reported in 19 UTUC samples from AA-exposed patients  14  ; a set of 6,066 substitutions observed in seven primary skin squamous cell carcinomas (COSMIC v67); a set of 3,026 mutations observed in 10 primary lung adenocarcinomas from heavy smokers  48  ; a set of 10,455 mutations observed in eight glioblastomas recurring in patients treated with temozolomide  49  ; a set of 288 mutations observed in eight primary astrocytomas  49  ; and a set of 378 mutations observed in four primary glioblastomas  50.
Annotations of mutation data. For all datasets (MEF and human sets), the chromosome number, genomic coordinates, reference and mutated nucleotides were extracted for each variants. Variants were annotated with AnnoVar (version 2013aug23) using refGene, knownGene, ensGene, cytoBand, genomicSuperDups, and dbSNP128 databases for the mm9 mouse genome build. The human sets were annotated using additional databases: gwasCatalog, 1000 Genomes Project, NHLBI GO Exome Sequencing Project (ESP), COSMIC, dbSNP137 (hg19 build), and PolyPhen and SIFT databases for predicting the functional impact of mutations. Gene strand orientation was retrieved from the UCSC Genome Browser database using a Perl script developed by Heng Li at the Sanger Institute. Mutations were included in the analyses only if they could be successfully annotated.
Variants present in the dbSNP128 polymorphism database were excluded. The comprehensive lists of all SBS identified in all MEF conditions and SBS from human tumour datasets are available as Supplementary Dataset 3. Functional annotation analysis. A comprehensive list of established cancer driver genes (oncogenes and tumour suppressor genes) and candidate drivers coding for modifiers of DNA, histones and regulators of chromatin structure was assembled from literature and somatic mutation database mining  1, 25, 26. Selected gene classes were annotated with functional domain information obtained from the UniProt and ENSEMBL databases. The comprehensive list of functional gene classes was matched against genes with mutations found in all MEF cell lines. Non-synonymously mutated genes were further selected, considering both exposure-specific alterations and any other mutation type.
Human orthologues of the selected genes were examined in the COSMIC database for frequency of mutations in human tumours. For oncogenes, positions corresponding to non-synonymous mutations in MEFs were identified in human orthologues and investigated for mutation status in the COSMIC database. Gene Ontology Biological Process analyses were performed with the NIH DAVID web tool using default settings. Statistical analyses. Statistical analyses were performed using the free R software (R Core Team, 2013) v3. 0. 2 or Excel. For the strand bias analyses, the statistical difference in the number of SBS between the non-transcribed and the transcribed strand was evaluated through the Pearson's x2 test, using the prop. test function available in the stats R package. The test evaluated, for each experimental condition, whether the proportions of SBS in the non-transcribed strand differed from 0.
Cells and samples are represented by round and squared solid symbols, respectively. (b) Same as in (a) but with the human tumour datasets (same as shown in Figure1) added to the input and labelled by arrows. HxAID-Tg samples are omitted in (b) as no corresponding relevant tumour data were identified. AAN_UTUC, aristolochic acid nephropathy-related upper urinary tract urothelial carcinoma; GBM_TZM, glioblastoma after temozolomide treatment; Lung_Ca, lung carcinoma; Skin_SCC, skin squamous cell carcinoma. (c) Graphical representation of the similarity distance of each of the six MEF signatures (front-back axis) to each of the 27 human cancer signatures 2 (horizontal axis, 1A through U2). The vertical axis measures the similarity of signatures between the two systems, expressed as negative log(tan(angle), see Methods.
A similar full phenotypic complementation was previously reported for TAP-tagged RRP41  [5]. Complementation of the null mutation by both RRP41-myc and RRP41-GFP suggested that both fusion proteins can be integrated in the core exosome complex. To test this hypothesis and identify potential exosome co-factors, tagged-RRP41 and associated proteins were affinity-purified using superparamagnetic particles coated with anti-myc or anti-GFP monoclonal antibodies, respectively. As shown for myc-tagged RRP41 IP, a specific group of proteins were visualized on silverstained SDS-PAGE gel as compared with mock IP (Fig. 1 ). The proteins co-purifying with RRP41 were identified by mass spectrometry (nano LC-MS/MS) analysis. A final list of 14 proteins was established by excluding proteins present in mock purifications and by crossing the datasets of three biological repeats (Table  1 ).