dataesr/scientific-paragraphs-categorization huggingface dataset:
These genes are therefore of particular interest considering their implication in the response of juvenile rainbow trout to EE2 treatment. The bioinformatics workflow applied in this study was previously shown to be effective in retrieving pathways in order to focus on potential novel genes associated with cancer metastasis and hypoxia in humans  [19, 22, 23] , and to identify potential biomarkers of intersex in rainbow trout  [24]. Intersections between groups of DEGs highlight genes implicated in the response of juvenile rainbow trout to exogenous EE2 treatment. To focus on relevant genes related to the male-tofemale transdifferentiation process, we further filter on phenotypic anchoring, which consists of encompassing different levels of biological organization  [11]. We choose the CT1 and CT2 contrasts as the most relevant in dataset 1 as they represent mostly fishes in the intersex stage  [25].
In dataset 2, the stages covering early differentiation, before gametogenesis, (D0 to D30) were chosen as the most relevant in the context studied  [29]. Since sexual dimorphic gene expression patterns have been shown to appear from 35 dpf in rainbow trout  [34, 35] , the earliest impact of EE2 on rainbow trout differentiation processes has to be considered. GO Terms and biological pathways helps interpreting gene expression data in terms of the affected biological processes, pathways and phenotypic changes and on the mode of action and toxicity of a compound  [36]  including exposure to EDCs  [6, 37, 38]. However especially in fish, namely here the rainbow trout, ORA approaches lacks of resources, genome annotations being scarce and/or heterogeneous. We thus chose to work by gene homology with Danio rerio, the only fish for which accurate pathways maps were available on Kegg  [39].
Relevant genes potentially implicated in the early male-to-female transdifferentiation process The final step of our analysis retrieved 20 statistically and physiologically relevant genes potentially implicated in the male-to-female transdifferentiation process in trout. Indeed, those genes are expressed shortly after EE2 treatment, at early developmental stages (7-16 and 30  days after the treatment, with the fish being at 55 dpf on day 0), and in intersex fish following chronic exposure to low doses of EE2 (namely 0. 01 and 0. 1 μg/L). Among those genes, ten (kidins220, tekt1, gsta, hsd17b6, aox1, lamb1, cyp17a1, tbx1, cyp11b and lim2) were found to be also differentially expressed during normal female development compared to control males, at early developmental stages (D0 to D30 in dataset 2), with a very similar pattern (Fig 5A  -5J ).
Most of these genes were underexpressed, except gsta and hsd17b6. Only the expression patterns of kidins220 and tekt1 were non-similar, as they were overexpressed in dataset 1 and underexpressed in dataset 2. The other ten genes displayed expression patterns specific to the EE2 treatment (vtg, rsph1-3-9, pltp, foxf1, αtub84d, p4hb, csrp2, arlip6) (Fig 5K  -5T ). They all showed overexpression compared to control males (except p4hb and csrp2), with a peak at D30 in dataset 2. Female expression was similar to that of control males (except for p4hb). This indicates that alterations in the expression of genes naturally involved in the early female differentiation process may be involved in the early EE2-induced feminization. 4. 3. 1. Genes involved in natural early female differentiation. Three of these genes encode key steroidogenic enzymes involved in 11-oxygenated androgen (including 11-KT) biosynthesis in the testis  [55].
Cyp17a1 and hsd17b are underexpressed following a short period of exposure to E2 and EE2 in male fathead minnows, whereas their expression is overexpressed in females  [60]. Inversely, hsd17b was overexpressed in our results, in keeping with its natural overexpression observed in females (Fig  5H ). Several types of 17βHSD enzymes exist and their relative roles have not been well defined  [65]. Considering that testosterone is the precursor of E2, this enzyme is also implicated in regulating estrogen availability. It is then unclear why this enzyme is overexpressed. However, decreased levels of 11KT were recorded in exposed fish from dataset 1 (article 1). Altogether, the presence of these three genes among our candidates confirms their important role as key regulators of the feminization process, probably through a decrease in androgen levels.
More importantly, feminization seems to rely essentially, though not exclusively, on the quick and strong decrease in cyp11b expression, whose expression profiles and significance are consistent in all the contrasts considered in our meta-analysis (Fig  3 ). Moreover, cyp11b has already been identified as a key actor triggering the male-to-female transdifferentiation process  [8, 64]. Another early testicular marker of testis development (tbx1) was found among the relevant genes, and was strongly underexpressed in both analyses (dose-dependently in dataset 1 -Fig 4A ; and with a peak at D30 in dataset 2-Fig 5D ), which is consistent with previous studies showing strong downregulation of tbx1 by EE2  [66]. tbx1 is a member of the Tbox transcription factor gene family involved in embryogenesis and organogenesis, whose expression is restricted to the somatic cells in the testis and appears to be sexually dimorphic very early in differentiating gonads (from 35 dpf)  [66, 67].
In mice, this gene seems to play a role in spermatogenesis and tekt1 mRNA are measured in spermatocytes and spermatids within seminiferous tubules  [69]. Several other genes sharing similar expression profiles with normal females, have never, to our knowledge, been reported as being directly involved in sex differentiation or feminization. Aox1 belongs to a highly conserved family of proteins with oxidoreductase activity. The physiological functions and substrates of AOXs are still unknown, although they are recognized as major drug metabolizing enzymes  [70]. The expression profiles of the Lim2 gene are of particular interest, as it was strongly underexpressed in both datasets, with increased repression between D7 and D111. It was reported in the enriched "Organogenesis" GO Term in our previous study  [24].
4. 3. 2. Genes specific to EE2 exposure. Among those genes, the well-known biomarker of estrogen exposure VTG was selected, which confirms the validity of our analysis to retrieve important genes responding to EE2 treatment. The gene αtub84d encoding a cytoskeleton component was found to be overexpressed during spermatogenesis  [42]  and the gene product of ARL6IP1 was reported to be involved in protein transport, membrane trafficking or cell signaling during hematopoietic maturation in vitro  [75, 76]  and in neural crest development in zebrafish embryos in vivo  [77]. Its in vivo roles during embryonic development are still largely unknown. Three genes encoding members of the radial spoke head protein family (rsph 1, rsph 2 and rsph 9) were selected in our meta-analysis. They were found to be upregulated by the EE2 treatment in both datasets, with a peak of expression between D16 and D91 in dataset 2.
foxf1 belongs to the family of forkhead transcription factors, known as key players in development and metabolism (foxl2 is a important gene triggering female differentiation and development in fish)  [82]. foxf1 expression during early development in rainbow trout is located mainly in the digestive tract and gills, and is associated with the development of gill filaments and lamellae  [83]. The presence of ERE sequences in the promoter region of several fox genes (including foxf1) suggests that their transcriptional regulation is under ER control  [84]. This is supported, at least for foxf1, by our results showing overexpression of this gene in both datasets. Cysteine and glycine-rich protein 2 (csrp2) is involved in cell differentiation. Its expression is naturally dimorphic during early differentiation, and it is underexpressed in females.
This allowed creating different groups of DEGs (Intersections I, II and III) for ORA analyses downstream, given that pathway or Ontologies research have different constraint concerning the group of genes to enter in the program. It is worth noting that each group of DEGs has different biological meaning described further in the text in the results section. Intersection I (low stringency) contained the intersection between all DEGs in the four contrasts in dataset 1 (CT1 to CT4) and all DEGs in the twelve contrasts including EE2-treated fish in dataset 2 (MT-MC D7 to D111, and MT-FC D7 to D111). This group contained 1,535 DEGs (1,171 DEGs having a functional annotation with Danio rerio Ensembl! identifiers-ENSARG) and was further used in the pathway overrepresentation analysis. A smaller group of DEGs (Intersection II) was obtained by retrieving the intersection between all DEGs in the four contrasts in dataset 1 (CT1 to CT4) and all DEGs in the six contrasts including EE2-treated fish versus male control fish in dataset 2 (MTMC D7 to D111).
This group contained 438 DEGs (245 DEGs having a functional annotation with Swissprot identifiers) and was further used in the ontology overrepresentation analysis. The third group (Intersection III-highest stringency-103 DEGs) was composed of the union intersection between contrasts CT1 and CT2 in dataset 1 and MT-MC D7, MT-MC D16 and MT-MC D30 in dataset 2. From this last list of DEGs, a set of most relevant DEGs was selected using volcanoplots, membership of enriched pathways and/or GOterms and kinetic profiles in both analyses. The 20 remaining genes, considered as potentially implicated in the transdifferentiation processes, were selected for further analyses.
represent DEGs. As an example, CYP11B is represented by its label. The green bars represent the log 2 (+/-2) fold change and the blue bar represents a pvalue threshold of 0. 05. (A) Selected DEGs in contrast MTMC D7 in dataset 2. (B) Selected DEGs in contrast MTMC D16 in dataset 2. (C) Selected DEGs in contrast MTMC D30 in dataset 2. (D) Selected DEGs in contrast CT1 in dataset 1. (E) Selected DEGs in Contrast CT2 in dataset 1. doi:10. 1371/journal. pone. 0135799. g003.
Acknowledgments The authors are grateful to  J. Montfort  and  A. Le Cam  from the  INRA-LPGP/BIOSIT (Rennes, France)  functional genomics platform for their help in the microarray experiment procedures. Thanks are also extended to the entire  URBM bioinformatics team  for their support in several of the statistical and bioinformatics steps of this study. Finally, we thank  François Moreews  from the  Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA)  for his valuable help regarding the rainbow trout platform annotation. This work is supported by the  FNRS (S. Depiereux) ,  FRIA  ( M. Pierre ) and  FRS-FNRS Télévie  ( B. De Meulder ).
Funding: SD was funded by a grant from the  'Fond de la recherche scientifique', (FNRS) : http://www. fnrs. be/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work is supported by the  FNRS (SD) ,  FRIA (MP)  and  FRS-FNRS Télévie (BDM).
MetaNetX is a repository of genome-scale metabolic networks (GSMNs) and biochemical pathways from a number of major resources imported into a common namespace of chemical compounds, reactions, cellular compartments--namely MNXref--and proteins. The MetaNetX. org website (http://www. metanetx. org/) provides access to these integrated data as well as a variety of tools that allow users to import their own GSMNs, map them to the MNXref reconciliation, and manipulate, compare, analyze, simulate (using flux balance analysis) and export the resulting GSMNs. MNXref and MetaNetX are regularly updated and freely available.
To permit simulations, a GSMN usually includes artificial reactions that describe the growth medium, a growth equation (which implies the com-position of the biomass) and possibly hypothetical reactions not (yet) supported by experimental biology but required to make a model functional. A relatively small number of high quality GSMNs have been published to date, essentially for model organisms, and are made available by a few dedicated databases  (2) (3) (4) (5) (6). The development of such models requires significant human effort and curation, and the fully automated reconstruction of a GSMN from an annotated genome sequence remains a challenge  (7, 8). Such methods require the integration of high quality curated data covering the known biochemistry of a vast range of organisms, as well as methods that address the specific requirements of a functional GSMN, including the elemental balancing of individual reactions.
MNXREF RECONCILIATION The metabolite identifiers found in the early-published GSMNs were often specific to the individual groups developing and curating them, and did not generally reference the major databases of chemical compounds. In recent years there have been a few attempts to 'reconcile' the different nomenclatures of these compounds (9,10) including our own effort MNXref  (11). The principles of the reconciliation algorithm used in MNXref can be summarized as follows: (i) Reconciliation of common metabolites based on chemical structures; (ii) Reconciliation of metabolites through shared chemical nomenclature; (iii) Reconciliation of reactions through shared metabolites; (iv) Identification of candidate reactions for reconciliation through shared cross-references; a BiGG and The SEED distribute collections of metabolites and reactions that are not necessarily retrieved in one of their GSMN.
(v) Iterative reconciliation of metabolites through reaction context. Figure  1  illustrates the reconciliation process using malonyl-CoA as an example; individual steps in the reconciliation are color-coded according to the type of evidence used. Table  1  summarizes the overlaps between the various sources of biochemical data and GSMNs according to the results of the MNXref reconciliation. The MNXref names-pace is regularly updated with metabolite and reaction data from new resources; recent additions include the EAWAG-BBD/UMBBD pathway database  (12). In the construction and use of GSMNs, every reaction must be balanced with respect to elemental composition and charge; failure to balance reactions will lead to violations in mass conservation that can have detrimental effects on the downstream simulations.
METANETX REPOSITORY AND TOOLS MetaNetX. org (  13 ) is a website that provides free access to the MNXref reconciliation data and a collection of published GSMNs and biochemical pathways mapped onto MNXref. The website also allows users to upload, manipulate, analyze or modify their own GSMNs and export them in SBML or in our own tab-delimited format. MetaNetX. org also offers a selection of tools for analyses including network structure, FBA or nested pattern methods  (14). Nucleic Acids Research, 2016, Vol. 44, Database issue D525 Gene names have been widely used in published GSMNs to describe the protein complexes that act as enzymes or transporters. Gene nomenclature is, however, essentially organism specific, if not dependent on a particular genome assembly. In MetaNetX we use UniProt accession numbers  (15)  to identify gene products: it greatly facilitates the inter organisms comparison of GSMNs from different sources.
Although the MNXref reconciliation algorithm is essentially automated, the compilation of the MetaNetX repository requires some manual intervention and a certain number of editorial choices. This includes definition of an accepted list of species and strains that includes important model organisms. Preference is given to the most comprehensive GSMNs from external sources that use accepted standard formats and have sufficient protein coverage (full acknowledgement is given to these external sources). We are closely collaborating with Rhea  (16) , which is a database of manually curated biochemical reactions, as part of the ongoing effort to further improve the quality of annotation of our resource.
CONCLUSION The www. metanetx. org resource provides a comprehensive suite of tools for the analysis of genome-scale metabolic models, based on a single integrated namespace of metabolites and metabolic reactions that integrates the most widely used biochemical databases and model repositories -MNXref. The reconciliation process used in MNXref greatly simplifies the development and analysis of genomescale metabolic models, allowing users to concentrate on model analysis rather than the time-consuming problem of identifier mapping. Future developments will include the provision of tools and the integration of new resources such as the SwissLipids knowledgebase  (17) , which provides lipid structures and curated data on enzymatic reactions.
1 ). (with E as an integer number of planetary periods) and, for MEarth-South, the number of telescopes used. The opacities of binned points are inversely proportional to their assigned variances, representing their approximate weights in the model fit. The raw data and details of the fit are presented in Methods; g and i refer to the wavelength bandpasses used from the PISCO imager. a rocky planet transiting a nearby low-mass star 3 A search of extant data (4,208 observations over 333 nights) for periodic signals revealed a 1. 6-day candidate that included this event and reached a detection statistic  13  of 9. 1σ. Follow-up photometry of subsequent predicted transits with four MEarth-South telescopes, the TRAPPIST (TRAnsiting Planets and PlanetesImals Small Telescope) telescope  14  , and the PISCO (Parallel Imager for Southern Cosmology Observations) multiband imager  15  on the Magellan Clay telescope confirmed the transit signal as being consistent with a planet-sized object blocking 0.
Radial velocity observations and analysis We first gathered reconnaissance spectra of GJ 1132 with the CHIRON 60 spectrograph on the SMARTS 1. 5-metre telescope at CTIO. Once these spectra ruled out large radial-velocity variations corresponding to binary star companions, we began observations with the HARPS spectrograph on the La Silla 3. 6-metre telescope. We gathered 25 HARPS spectra of GJ 1132 between 6 June 2015 and 18 July 2015, using an exposure time of 40 min. The spectra span 380 nm to 680 nm in wavelength. For each observation, we constructed a comparison template by co-adding all the other spectra and measured the relative radial velocity as the shift required to minimize the χ 2 of the difference between the spectrum and this template  61, 62. Telluric lines were masked using a template of lines made with a different, much larger, data set.
Research. X. B. , X. D. , T. F. and A. W. acknowledge the support of the French Agence Nationale de la Recherche and the European Research Council. M. G. and E. J. are FNRS Research Associates. V. N. acknowledges a CNPq/BJT Post-Doctorate fellowship and partial financial support from the INCT INEspaço. N. C. S. acknowledges the support from the Portuguese National Science Foundation (FCT) as well as the COMPETE program. Author Contributions The MEarth team (D. C. , J. I. , Z. K. B. -T. , E. R. N. and J. A. D. ) discovered the planet, organized the follow-up observations, and led the analysis and interpretation. Z. K. B. -T. analysed the light curve and radial velocity data, and wrote the manuscript. J. I. designed, installed, maintains, and operates the MEarth-South telescope array, identified the first triggered transit event, and substantially contributed to the analysis and interpretation.
Acknowledgements We thank the staff at the  Cerro Tololo Inter-American Observatory  for assistance in the construction and operation of MEarth-South;  J. Winn  and  J. Berta-Thompson  for comments on the manuscript;  S. Seager  and  A. Zsom  for conversations that improved the work;  L. Delrez  for her independent analysis of the TRAPPIST data; and  J. Eastman ,  D. Dragomir  and  R. Siverd  for their efforts to observe additional transits. The  MEarth  Project acknowledges funding from the  David and Lucile Packard Fellowship for Science and Engineering , and the  National Science Foundation , and a grant from the  John Templeton Foundation. The opinions expressed here are those of the authors and do not necessarily reflect the views of the  John Templeton Foundation. The development of the PISCO imager was supported by the  National Science Foundation.
Code availability Analyses were conducted primarily in Python. Although not cleanly packaged for general use, for the sake of transparency we make the custom code used for transit and radial velocity fitting available at http://github. com/zkbt/transit. It relies on three freely available packages: eb (http://github. com/mdwarfgeek/ eb), emcee (http://github. com/dfm/emcee), and George (http:// github. com/dfm/george). Code used to generate the exoplanet population comparison is available at http://github. com/zkbt/exopop.
We generated strains with an N-terminal GFP fusion of CenH3 (Additional file 1: Figure  S1 ); C-terminal CenH3-GFP fusions were unsuccessful in our hands (data not shown). The GFP-tagged allele was inserted at the endogenous CenH3 locus and is under the control of the native promoter. Transformed strains were made homokaryotic by repeated single spore isolations. We confirmed correct integration of the GFP construct by an initial PCR screen followed by Southern analyses and appropriate translation of the GFP-tagged CenH3 protein by western blot analyses (Additional file 1: Figure  S1 ). To visualize and localize the centromeres of Z. tritici, we used fluorescence microscopy. We observed multiple discrete GFP-CenH3 signals concentrated in four to seven foci per interphase nucleus. This suggests that Z.
Purification of DNA was performed with AMPure XP beads (Agencourt, Beckman-Coulter). To size select samples were gel-purified and PCR amplified by 13 PCR cycles with Phusion polymerase (Finnzymes) and Illumina PCR primers. Libraries were sequenced on an Illumina HiSeq 2000 genome analyzer at the OSU Center for Genome Research and Biocomputing. Sequencing data were submitted to SRA under accession number SRP059394. ChIP tracks are viewable on a dedicated gbrowse server at http://ascobase. cgrb. oregonstate. edu/cgi-bin/gb2/gbrowse/ncrassa_public/.
Short read mapping and peak calling analyses Illumina reads were filtered and trimmed as previously described  [14] , but they were not trimmed from the 5′ end, resulting in 47-nt reads. Processed reads were mapped to the Z. tritici IPO323 reference genome  [11]  with Bowtie2 at standard settings  [42]. Mapping outputs were converted from ". sam" to ". bed" files using Samtools and Bedtools  [104, 105]. For visualization of mapped reads, alignment files from Bowtie2 were either processed with the genomecov tool from the bedtools package  [105]  and the Integrative Genome Viewer (IGV; http://www. broadinstitute. org/software/igv); [106]  or loaded on a dedicated public gbrowse server (http://ascobase. cgrb. oregonstate. edu/cgi-bin/gb2/gbrowse/ncrassa_ public/). For each ChIP sample (Additional file 2: Table  S1 ), we determined significantly enriched domains with RSEG  [43].
Acknowledgements We thank  Mike Cusaki  for providing the IPO323∆KU70 strain and a binary cloning plasmid, and  Michael Bölker  for helpful discussions. This study was supported by intramural funding of the  Max Planck Society  to EHS and by  NIH  grant  GM097637  to MF. KS acknowledges an EMBO short-term fellowship for studies in Oregon. JLS is funded by a Young Scientist Funding by  INRA. KMS was partially supported by  NIH  Grant  GM068087.