dataesr/scientific-paragraphs-categorization huggingface dataset:
Databases of genome-wide protein-protein or domain-domain interactions are currently incomplete. We therefore expect the real PPV to be even higher than the one measured in Fig 2 : strongly coupled domain-domain pairs not belonging to our list of positives may actually be considered as predictions for new, currently unknown relations. According to the observations in Fig 2C, these relations might be direct physical interactions, but also genomic co-localization (frequently related to joint biological function). Before exploring these possibilities in more detail and on the finer scale of the residue-residue coevolution, we compare the PhyDCA results to phylogeny-aware correlation analysis and investigate the negative tail of the J ij distribution.
Comparison of phyletic couplings to phylogeny-aware analysis of correlated presence/absence patterns Phyletic couplings are, like simpler correlation measures, based on counting co-presence and co-absence of proteins or domains. However, due to the uneven phylogenetic distribution of species in our dataset, single evolutionary event may be amplified when appearing in an ancestor of several closely related species. More importantly in the context of this study, phylogeny may introduce spurious correlations in the presence and absence of domains, which are not related to biological function. To remove this bias, several methods have been proposed, cf. [11, 13] , which use evolutionary models to decide, if observed correlations can be explained by phylogeny alone (i. e. by independent evolution on a phylogenetic tree), or remain significant even when such phylogenetic effects are removed.
Second, we find domain pairs of the same fold and, where known, of similar activity. For various reasons these are not described by the same Pfam HMM (rankings  3, 4, 6, 7, 8, 10, 11, 17, 19) , but typically belong to the same Pfam clan indicating distant homology. Lastly, there are several cases of relatively unknown activity, and some domains have no known structure (rankings  5, 12, 13, 18, 20). Cases of convergent evolution include PF00303 and PF02511, which describe two different thymidylate synthases, the former a 5,10-methylenetetrahydrofolate, the latter a flavin dependent enzyme  [36]. Interestingly, PF00186, dihydrofolate reductase is also strongly negatively coupled with PF02511 (but positively to PF00303), since the former is not needed to regenerate 5,10-methylenetetrahydrofolate when the flavin-dependent enzyme is used.
Other cases of convergent evolution are PF01220 and PF01487 that describe two classes of dehydroquinases with similar activity but significantly different primary and secondary structure  [37]. PF00311 and PF02436 describe proteins in oxaloacetate biogenesis, the former from phosphoenolpyruvate, the later from pyruvate and ATP. PF00245 and PF05787 describe two classes of bacterial alkaline phosphatases, termed PhoA and PhoX with distinct protein folds  [38]. PF02502 and PF02436 distinguish two classes of ribose-or phosphoribo-isomerases with differing enzyme folds. Structurally similar proteins that are identified by different Pfam families are of less interest and will not be separately described. The fact that they are distinct enough in sequence to be covered by separate Pfam families suggests a level of divergent evolution, i.
e. one or the other domain has distinct features such us additional interaction partner, distinct activity regulation etc. Of special interest are domain pairs with unknown function. Ideally, if the function of one Pfam family becomes available one can infer the function of the other family as well. In addition, the evolutionary importance of a given protein family and its activity is often judged by its conservation across different phyla and organisms. This however neglects cases of unknown convergent evolution. Among the highest negatively coupled pairs, we did not find any, where the function of one has been clearly identified and the function of the other has not. However, there are several instances, where a potential role has been loosely associated with one or the other domain. For instance, PF01205 and PF09186 have been suggested to be involved in countering translation inhibition under starvation conditions  [39].
These domains are strongly negatively coupled with PF02594, suggesting that the latter might also serve a role in countering translation inhibition. PF01169 and PF02659 are both putative transporters, the former for calcium  [40] , the latter for manganese ions  [41]. Their coupling suggests overlapping specificities or roles. PF02677 and PF08331 describe two entirely unstudied bacterial proteins. The later appears associated with iron-sulfur cluster domains, suggesting a potential role in redox regulation. Lastly, we find a negative coupling between domains PF02696 and PF03190. Both proteins are entirely unstudied in bacteria, but they are also common in Eukaryotes where the latter is a proposed redox protein that has been implicated in fertility regulation in mammals  [42]. It would be interesting to unveil their function in the bacteria.
Many predictions of domain-domain interactions resulting from PhyDCA and residue-level DCA are biologically interpretable Domain pairs with both strong PhyDCA and residue-level DCA signals are our strongest candidates for predicted domain-domain interactions. Since they are not co-localized in the same protein, they also provide predictions for new protein-protein interactions. We analyze here in detail the 24 pairs with a DCA score larger than 0. 2, which result from the first 200 PhyDCA predictions. Among these 24 pairs we find several examples of known interactions that have not yet been structurally resolved. These include K + transporter subunits KdpC (PF02669) and KdpA (PF03814)  [45] , Sigma54 activator (PF00158) and Sigma54 activator interacting domain (PF00309)  [46]  and exonuclease VII subunits domains PF02609, PF2601 and PF13742  [47].
For several additional positively coupled pairs an interaction seems functionally very likely but to our knowledge no interaction studies are available. These are all proteins involved in pilus formation or maturation. Domain PF06750 is a putative methyl transferase domain in the prepilin peptidase PppA, and proposed to interact with methylation motif domain PF07963, found in numerous pilin proteins and with PF05157, a type II secretion system protein  [48, 49]. PF05157 is also predicted to interact with domain PF05137 found in the PilN fimbrial assembly protein required for mating in liquid culture  [50]. Of interest, there are predicted interactions for several members of biosynthetic pathways catalyzing either consecutive or closely following reactions. These include domains PF02542 and PF13288 of isoprenoid biosynthesis enzymes Dxr and IspF, domains PF00885 and PF00926 of riboflavin biosynthesis enzymes RisB and RibB and domains PF01227 and PF01288 of tetrahydrofolate biosynthesis enzymes Gch1 and HppK.
A more complex connection is predicted between multiple domains of molybdenum cofactor biosynthesis enzyme MoaC (PF01967), MoeA (PF03453 and PF03454) and MoaA (PF06463). Similarly, scores suggest a protein-protein interaction between domains of hydrogenase maturation enzymes HypF (PF07503) with HybG (PF01455) and HycI (PF01750). Perhaps most intriguing are the observation of strongly coupled co-occurrence and potential protein-protein interactions of two proteins pairs. Ada (PF02805) and AlkA (PF06029) are two enzymes involved in DNA repair in response to alkylation damage  [51, 52]. One of the proteins serves as demethylase of guanosyl residues whereas the other excises alkylated nucleotides. These seemingly complementary functions suggest that an interaction is plausible. The other pair is YoeB (PF06769) with HicA (PF07927).
These two proteins constitute two toxins of distinct toxin-antitoxin systems. Both proteins inhibit translation by distinct and complementary mechanisms and an interaction seems plausible. YoeB blocks the ribosome A site leading to mRNA cleavage  [53]. HicA interacts with mRNA directly and thus acts independent of the translation apparatus  [54]. Additional and perhaps plausible interactions are predicted between domains PF05930 and PF13356 of prophage protein AlpA and several phage integrase proteins as well as between domain PF13518 with PF13817, the former a HTH domain commonly associated with transposase domains and the latter a transposase domain. Insufficient information on the function of two domain pairs and their associated proteins does not allow us to draw any conclusions on the plausibility of interaction.
These are for domains PF02021 and PF13335 of proteins YraN and YifB and domains PF01906 with PF02796, the former a metal binding domain and the latter a domain found in site specific recombinases. Lastly, we find three proposed interactions between domains found in ribosomal proteins RL36, RL34 and RL32 (PF00444, PF00468, PF01783) and also a protein of unknown function YidD (PF01809). We consider these to be likely false positive predictions since we previously observed spurious results for members of very large macromolecular complexes such as the ribosome  [10]. At least the interaction between YidD and RL36 seems plausible, as the former has been suggested to play a role as membrane protein insertion factor  [55]. In summary, we are able to recapitulate several known or plausible but structurally unresolved interactions and find several examples of interaction that should be of interest for future experimental studies.
In contrast the positive predictive value of phylogenetic couplings reaches about 80%. The results are very robust: when applying the same methodology to all 9358 Pfam domains appearing in the bacteria, and selecting only later the couplings between domains present in E. coli, couplings have 96% correlation with the couplings found by the procedure described before. The high accuracy of phyletic couplings in predicting domain-domain relations, along with the robustness of these couplings when extensively changing the data set, allows us to hypothesize that large couplings not corresponding to known relations predict novel, unknown relations. A list of the 100 first predictions is provided in Tables A and B in Supplement S1 Text. As mentioned, a large phyletic coupling does not automatically imply a direct physical interaction.
Bacteria, due to their compact genomes, are overrepresented in genomic databases, including the Pfam database, which we used for our analysis. To test the applicability to higher organisms, we have repeated the same procedure, concentrating on eukaryotic genomes and taking humans as the reference species. Data get much less abundant; the phylogenetic profile matrix now contains 5343 domains as compared to only 481 eukaryotic species. Still, phyletic couplings, when compared to a positive list extracted from domain architectures of human proteins (co-localization in one protein), from iPfam  [44]  and human entries in IntAct  [56] , show a similar performance as the bacterial case, cf. To conclude, our work illustrates the potential of combining rapidly growing genomic databases and statistical modeling: the increasing number of fully sequenced genomes allows for extracting rich samples for the variability in protein content and protein sequences across hundreds and thousands of species; their statistical analysis helps us to detect multiple scales of coevolution between interacting or functionally related proteins.
Phylogenetic profiles Data are extracted from the Pfam 30. 0 database  [31]. For each of the 1,041 bacterial genomes present in Pfam, we extract all appearing protein-domain families, accounting to a total of 9,358 Pfam families. A restriction to Escherichia coli as reference genome (i. e. counting only domains contained in E. coli) reduces this to 2682 domain families. Since we are interested in the correlated presence / absence of domains across species, we remove all domain families with less than 5% or more than 95%, keeping only domains with at least 53 and at most 988 appearances. This removes in particular omnipresent domains related, e. g. , to replication, transcription and translation. The final phylogenetic profile matrix (PPM) is a binary matrix containing M = 1,041 bacteria and N = 2,041 domains.
Known domain-domain relationships To assess the accuracy of our predictions, we have compiled a number of known relationships (provided in Supplement S1 Text). They come from different databases, the same domaindomain pair may appear multiple times, but it is counted only once in the final list of positives: 1. Intra-protein localization: From the Pfam database  [31] , we have extracted a list of domain pairs, which co-occur inside single proteins in E. coli. Out of 3,116 proteins, 952 contained multiple domains, giving rise to 799 distinct domain-domain relations. 2. Intra-operon localization: Proteins, which are co-localized inside operons, frequently share at least part of their biological function. Using a list of operons from E. coli  [60] , we compiled a list of 4,087 colocalized domain pairs.
MW acknowledges funding by the  EU H2020  research and innovation programme  MSCA-RISE-2016  under grant agreement No. 734439  INFERNET. HS was funded by Grant  GM106085  from the  National Institute of General Medical Sciences, NIH. This work undertaken partially in the framework of CALSIMLAB and supported by the public grant  ANR-11-LABX-0037-01  overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( ANR-11-IDEX-0004-02 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
8 NA water immersion objective. The donor excitation was provided by an LED source (420 nm diode, Mightex) with a HC434/17 excitation filter (Semrock). The dichroic mirror was BS452 (Semrock). Two images were acquired for every data point by alternating filter for donor (HC479/40) and acceptor (HC542/50 both from Semrock) emission with a filter wheel (Sutter Instruments). Images were acquired using an ORCA-AG camera (Hamamatsu). Image acquisition was controlled using iVision software (Biovision, RRID:SCR_005057). Data analysis was done with custom routines written in IGOR Pro (Wavemetric, RRID:SCR_000325) following the previously described protocol  (Polito et al. 2014; Yapo et al. 2017). FRET changes were quantified using ratio of images from donor and acceptor channels for each data point.
Acknowledgments This work was supported by  Inserm ,  Fondation pour la Recherche Médicale  (FRM) and The  French National Research Agency ANR-DOPAFEAR  ( EV ),  ANR-SEXREV (FV). GG is supported by  Allen Foundation Inc. ( 2016. 326 ) and  Nutricia Research Foundation  ( 2017-20 ) grants. The authors thank the animal breeding facility of Montpellier University (CECEMA).
c. #2073 RRID:SCR_003689 (Gangarossa et al. 2013c) SCH23390 0. 1 mg/kg, i. p. #0925 RRID:SCR_003689 (Valjent et al. 2000) LiCl 150 mg/kg, i. p. #L4408 RRID:SCR_008988 (Longoni et al. 2011) DOI 10 mg/kg, i. p. #D101 RRID:SCR_008988 (Halberstadt et al. 2009) Phencyclidine 3 mg/kg, s. c. #P3029 RRID:SCR_008988 (Mouri et al. 2012) Dizocilpine 0. 3 mg/kg, i. p. #0924 RRID:SCR_003689 (Valjent et al. 2000) PTZ 60 mg/kg, i. p. #P6500 RRID:SCR_008988 (Assaf et al. 2011) Kainate 20 mg/kg, i. p. #K0250 RRID:SCR_008988 (Blüthgen et al. 2017) Papaverine 30 mg/kg, i. p. #P3510 RRID:SCR_008988 (Nishi et al. 2008) Rolipram 10 mg/kg, i. p. #0905 RRID:SCR_003689 (Nishi et al. 2008) SKF38393 1 µM #0922 RRID:SCR_003689 (Polito et al. 2015) CGS21680 1 µM #1063 RRID:SCR_003689 (Polito et al. 2015) Forskolin 12.
Introduction: Models of dose-effect relationships seek systematic and predictive descriptions of how cell survival depends on the level and duration of the stressor. The CEM43 thermal dose model has been empirically derived more than thirty years ago and still serves as a benchmark for hyperthermia protocols despitethe advent of regulatory network models. Objective: In this paper, we propose and realize a simple experimental test to assess whether mechanistic models can prove more reliable indicators for some protocols. We define two time-asymmetric hyperthermia profiles, faster rise than decay or slower rise than decay, for which the CEM43 model predicts the same survival while a regulatory network model predicts significant differences. Materials: Experimental data (both control 37 C and hyperthermia assays) were collected from duplicate HeLa cell cultures.
Hyperthermia experimental assays Experimental data (both control 37 C and hyperthermia assays) were collected from duplicate HeLa cell cultures. Culture dishes were maintained in a custom-built incubator wherein temperature, humidity and atmosphere are monitored. The incubator, described in  [13] , contains four culture dishes where one of the dishes hosts a thermistor which monitors the applied temperature, two dishes with two different cell culture samples were used for cell survival experiments (see Figure  S14  of the Supplemental Material for an incubator picture). Measuring the inertial time scale of the incubator, allows to predict the time profiles that deliver equivalent CEM43 doses. In addition, a trial-error procedure had to be performed to obtain two triangular temperature profiles with equivalent CEM43, since the precise temperature depends on the room temperature conditions.
This work was funded by  NCCR 'Frontiers in Genetics' (fellowship to VI) , the  Swiss National Science Foundation  (grant no  31003A 153331  to FS),  Polish Swiss Research Programme PSRP NoCore  ( 183/2010  to FS), iGE3 and the  Canton of Geneva. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgements This publication is based upon the work from the  EU COST Action ADMIRE BM1301 in Aldosterone and Mineralocorticoid Receptor Physiology and Pathophysiology  (www. admirecosteu. com). The authors' work was supported by grants from the  Institut National de la Santé et de la Recherche Médicale , the  Centre de Recherche Industrielle et Technique , the  Agence Nationale de la.
Although this domain was predicted to be a putative RNA binding domain since its identification, its RNA binding property was only demonstrated in 2010 when Zhang et al. showed that YTHDC1 can bind degenerate RNA sequences  [33]. YTH-containing proteins became a topic of strong interest when YTHDF1, 2, and 3, three paralogous members of the YTH-containing protein family, were found to be the most enriched human proteins specifically retained by an RNA fragment containing an m 6 A modification at the heart of the DRA*CH consensus sequence  [10, 26]. The YTH domain is made of 150 to 200 residues and adopts an α/β fold, with four to five α-helices surrounding a curved six stranded β-sheet as revealed by the NMR structure of YTHDC1 YTH domain determined by the RIKEN Structural Genomics and Proteomics Initiative in 2007 (PDB code: 2YUD).
Stereomicroscope images processing White and orange skins were imaged using Zeiss stereomicroscope (V20 discovery-Plan S objective 1. 0x) using a VisiLED brightfield ringlight S80 for incident lighting. All the images were taken using a full homogenized lighting. However, iridescent cells of the white skin were only visible when using segmental lighting. Images were processed using a camera (Axiocam 105) with a picture resolution of 1280×960 pixels. Pictures of high magnification of skin (Fig. 1  and Supp Fig. 1  and 2) were done using 30 ms of time exposure. Pictures of full individuals (Fig. 2 ), were done always in the same lighting conditions with an exposure time of 10 ms. White balance was set up at the beginning of each experiment to get the same images properties. A post-process was done on original images to increase contrast of images using ImageJ software.
Quantification, differential expression and gene set enrichment analyses We used kallistov0. 43. 0 to align reads originating from single-end skin samples to the reference transcriptome and to quantify transcript abundance. Estimated counts were imported in R using tximport package  (Soneson et al. , 2015). Differential expression (DE) analysis was performed using DEseq2v1. 14. 1 testing for color variations with the individual effect included in the design  (Love et al. , 2014). Gene Set Enrichment Analyses were performed with Java implementation of the GSEA software (http:// software. broadinstitute. org/gsea/index. jsp) (Subramanian et al. , 2014).
, 1995)  to diminish the numbers of clonal lineages carrying mutations and enhance survivorship. Mutagenicity of each sgRNA was confirmed by Sanger sequencing of amplicons from whole embryo genomic DNA. sgRNA targets are described in Supp. Table  4. F 0 embryos were raised at ~28 °C for 4 days at which time larvae were anesthetized with MS222 and iridophores at the dorsal midline were counted manually under incident light. Analyses of iridophore counts were performed using JMP 14. 0 statistical software (SAS Institute, Cary NC).
Fig. 3 ). Comparison of white and orange skin samples revealed 86 genes expressed at significantly higher levels in white skin and 83 genes expressed at significantly higher levels in orange skin (p < 0. 05; Benjamini-Hochberg adjusted p-value) (Supp. Table  3 ). Skin samples include pigment cells and a variety of other cell types. To gain further insight into the transcriptomic signature of A. ocellaris white skin, we compared our results to an available set of 346 genes specifically enriched in zebrafish iridophores  (Higdon et al. , 2013). Of these 346 loci, 237 were detectable in A. ocellaris skin samples (Supp. Table  5 ). Using this dataset we carried out two types of analysis: (i) We first performed a Gene Set Enrichment Analysis using all A. ocellaris genes detectably expressed in both sample types.
ocellaris, with the exception of ttc39b (Fig. 3C ). Among the 86 genes more highly expressed in white skin than orange skin, fhl2a and fhl2balready known to be expressed in iridophores  (Santos et al. , 2014)  -and gpnmb had higher expression in zebrafish iridophores than melanophores  (Higdon et al. , 2013). Apolipoprotein D (apoD1a) is expressed specifically in cichlids egg-spots but its function in pigmentation, if any, has not been characterized  (Gu & Xia, 2017). Finally, the gene with the second highest change in white skin was an ortholog to zebrafish gene ENSDARG00000055172 (Ensembl)/ 795494 (NCBI) (Fig. 3A ) with no associated function in Ensembl, NCBI or ZFIN databases. This gene is currently annotated as si:ch211-256m1:8 and we refer to it as saiyan in reference to the manga comics characters that can become shiny.
White bars appear late during postembryonic development, starting at stage 4 and being well visible at stage 6 (N. R. , P. S. and V. L. , in preparation;  Salis et al. , 2018). To test for an association between expression of these 5 genes and bar ontogeny, we used a transcriptomic dataset for skins across multiple post-embryonic stages. As shown in Supp. Fig. 5 , all 5 genes had minima of expression prior to overt bar development at stage 4, followed by increases through the climax of bar development at stage 6, and drops in expression by early juvenile stage 7. To assess the spatial localization of these transcripts and confirm the presence of all five genes in iridophores, we performed in situ hybridization on A. ocellaris white and orange adult skin. fhl2a, fhl2b, and apoD1a were expressed in the stratum laxum of the dermis, mainly composed of iridophores as observed in Fig.