dataesr/scientific-paragraphs-categorization huggingface dataset:
A variation of this technique is TGGE, which uses a thermal gradient to separate the groups maintaining constant urea and formamide concentrations. Band patterns can be evaluated by different softwares that normalize data and calculate abundance and richness of species through thickness and number of bands. Each band position is registered in a database where the comparison between DGGE gels is performed, but they must have the same denaturation gradient and migration time, that is, the gels must have a standardized methodology  67. Species of communities are identified by band excision from the gel followed by sequencing or hybridization with specific DNA probes. Genes such as 16SrRNA (around 1,500 nucleotides) serve as clone libraries associated to many bacteria groups in a variety of environments.
This pH change is detected by a transmitter and converted into an electrical signal. Directing the study for the microorganisms who had roles in the ecosystem, GeoChip, developed amicroarray method  78  , containing more than 24,000 probes and covers, 150 gene families involved in biogeochemical C, N and P cycling  79  an important tool in agricultural ecosystems. NGS use a more robust analysis and as a consequence, increase the analytical power of results, being more important in several projects involving genomics and metagenomics  70. Bioinformatics software are fast and are in steady development, increasing the amount of data evaluated and contributing to the construction of megabases, increasing, as well, the amount of information on various ecosystems.
7d ). In contrast, LPA-stimulated HIF-1α expression was insensitive to the presence of p110β class IA PI3K inhibitor TGX-221 (Fig. 7d ). To establish a role for the PI-3kinase pathway in LPA-stimulated HCV infection we assessed the ability of these inhibitors to modulate infection. WM and BYL-719 treatments ablated the LPA enhancement of HCV infection whereas TGX-221 had no effect (Fig. 7e ). In summary, these data demonstrate a role for LPA-activated PI-3-kinase signalling in stabilizing HIF-1α that regulates HCV replication (Fig. 7f ). Discussion: Our studies uncover a role for the ATX-LPA signalling axis to positively regulate HCV RNA replication by activating PI3K and stabilizing HIF-1α (Fig. 7f ). Inhibiting ATX activity or LPA signalling reduced HCV replication, providing evidence for an autocrine LPAfeedback loop to promote viral replication.
Infectivity is expressed relative to untreated cells and represents the mean of three replicate infections. (b) Huh-7 cells stably expressing the Luc2A-JFH replicon were treated with HA130 (100nM) or transduced to express shControl or shATX for 24h, cells lysed and luciferase activity measured. Data is expressed relative to untreated and represents the mean of three replicate infections (***p<0. 001). (c) Huh-7 cells were transfected with HCV J6/JFH or a polymerase replication defective genome (GND -) that encodes gaussia luciferase (GLuc) and the cells grown under serum-free conditions for 8h prior to treating with HA130 (100nM). Extracellular media was collected at 0 and 24h post HA130 treatment and luciferase activity measured (***p<0. 001). Viral replication is assessed by determining the ratio of J6/JFH/GND - luciferase activity and represents the mean of three replicate infections.
Bound antibody was detected with rabbit anti-rat secondary for 15mins, ImmPRESS rabbit secondary for 30mins and visualised using ImmPACT DAB (Vector Labs, UK) and counterstained with Meyers haematoxylin. RT-PCR quantification of ATX and HIF-target genes. Gene amplification was performed in a single tube RT-PCR in accordance with manufacturer's guidelines (CellsDirect kit, Invitrogen, CA) and fluorescence monitored in a 7900HT real time PCR machine (ABI, CA). The housekeeping gene GAPDH was included as an internal control for amplification efficiency and RNA quantification (primer-limited endogenous control, ABI). HCC liver samples, clinical data and gene expression data. We used an Affymetrix U133A2. 0 gene expression data set derived from 247 HCC patients as described  [16]  (GSE14520). Patient samples were obtained with informed consent from patients at the Liver Cancer Institute and Zhongshan Hospital (Fudan University, Shanghai, China).
Acknowledgements: We thank  Takaji Wakita  for  HCV J6/JFH ,  Jens Bukh  for HCV SA13/JFH Charlies Rice for anti-NS5A mAb  9E10 ,  Robert Thimme  for Luc2a-JFH replicon cells and  Samantha Lissaeur  for differentiation protocols. Statistical analysis on mass spectrometric data reported in Fig. 4a was performed by  Dr. Anne Segonds-Pichon  at the bioinformatics facility at the  Babraham Institute. Research in the McKeating laboratory was funded by the  MRC, NIHR Birmingham Liver BRU ,  EU   FP7 PathCO  and  H2020  grant  Hep-CAR. Research in the Wakelam lab  is supported by  BBSRC and Hep-CAR. Stephanie Roessler  was supported by  Hep-CAR ,  DFG  grant  RO4673 , the  Olympia-Morata Programme , a  Brigitte-Schieben-Lange Fellowship  and a  Heidelberg School of Oncology Fellowship.
Histology Mesenteric fat samples and liver were embedded in paraffin and cut in 10μm and 3µm sections respectively. Sections were then stained with hematoxylin and eosin. Mesenteric fat sections were examined under a light microscope (Nikon DS-Ri2) and images were taken at 100x magnification using NIS-Elements software. The area of adipocytes was measured with ImageJ 1. 50i digital imaging processing software. Images from liver sections were randomly taken at 20x magnification under a light microscope (Nikon DS-Ri2). Image analysis using dedicated software (NIS-Elements AR3. 0 software, Nikon Instruments) was performed to automatically detect lipid droplets and quantify their surface. Ileum and colon samples were embedded in paraffin and cut in 5-µm sections. Both sections were then stained with periodic acid-Schiff-alcian blue (PAS/AB) and examined under a light microscope (Nikon ECLIPSE E400; Nikon Instrument) equipped with image analysis software.
Statistical analysis Statistical analysis was performed on Graphpad Prism software (v5, San Diego, CA, USA) and data are expressed as means ± SEM. Data were analyzed using two-ways ANOVA testing diet, feeding pattern and diet X feeding pattern, with Bonferroni post hoc tests. For body weight and food intake analysis, diet, feeding pattern, time, diet X feeding pattern effects were analyzed by ANOVA using R software. P<0. 05 was considered significant. Correlation analysis between data was performed using Graphpad Prism.
Six week WF feeding profoundly affects ileal barrier function Mucus secreted by GC is the first line of defense of the intestinal mucosa, limiting the presence of noxious molecules such as LPS on the apical side of epithelial cells. We therefore evaluated the number of GC in ileal and large intestinal mucosa using PAS/AB staining that colors mucins. In the ileum, the number of GC per villus or per crypt was reduced in WD rats (diet P<0. 001, Fig 4A  -B ) with a tendency for a more pronounced reduction in WDpf rats (WDal vs WDpf P=0. 07 in villi and P=0. 056 in crypts). Since villi length, but not crypt depth, was decreased in WD-fed rats, irrespective of initial energy intake (data not shown), we calculated a number of GC / µm of villus or crypt to ensure that the reduction in GC number observed was not due to reduced villi size.
Acknowledgments The authors are grateful to  Dr. Kristina Hamilton  for technical assistance in Ussing chamber experiments and rats handling. We acknowledge  Adelaïde Prevotel ,  Michèle Formal ,  Armelle Cahu ,  Gwenaëlle Randuineau ,  Sylvie Guérin  and  Paul Meurice  for technical assistance in histological analysis. We thank  Regis Janvier  for technical assistance in rats handling. We thank  Veronique Romé  and  Laurence Le Normand  for their help on molecular biology data analysis and colorimetric assays.
The protein universe corresponds to the set of all proteins found in all organisms. A way to explore it is by taking into account the domain content of the proteins. However, some part of sequences and many entire sequences remain un-annotated despite a converging number of domain families. The un-annotated part of the protein universe is referred to as the dark proteome and remains poorly characterized. In this study, we quantify the amount of foldable domains within the dark proteome by using the hydrophobic cluster analysis methodology. These un-annotated foldable domains were grouped using a combination of remote homology searches and domain annotations, leading to define different levels of darkness. The dark foldable domains were analyzed to understand what make them different from domains stored in databases and thus difficult to annotate.
The un-annotated domains of the dark proteome universe display specific features relative to database domains: shorter length, non-canonical content and particular topology in hydrophobic residues, higher propensity for disorder, and a higher energy. These features make them hard to relate to known families. Based on these observations, we emphasize that domain annotation methodologies can still be improved to fully apprehend and decipher the molecular evolution of the protein universe. The protein universe is a concept corresponding to the set of all possible protein sequences that can be found in all living organisms 1. Understanding how the current protein universe appeared and evolved are central questions in evolutionary molecular biology  [2] [3] [4]. Moreover, deciphering the rules behind the apparition of new proteins and their conservation in an organism may lead to develop improved methods for protein design.
If the protein sequence information is used, a protein domain then corresponds to the amino acid sequence part that is evolutionary conserved and for which similar sequences can be found in other proteins of the same or in different organisms 8. In the present study, references to protein domain will be based on this last, sequence-based definition, as it allows the largest coverage of the protein sequence space. In this sequence-based definition, the foldability is an optional characteristic; however, there is a good general agreement between the two types of domain definitions, as assessed by the comparison of sequence-based and structure-based domain databases  13, 14. Several protein domain databases  [5] [6] [7] [8] 15  have been developed allowing automatic protein annotation. The  give a broad view of the evolutionary mechanisms at play in the emergence, disappearance and re-use of the evolutionary units that are protein domains.
matrices (PSSMs) are usually built on a protein domain alignment seed, and this seed is afterward used to find occurrences of the domain in other protein sequences. Software and databases using domain HMMs/PSSMs can achieve great sensitivity and are able to annotate protein sequences sharing less than 20% of similarity  16. However, the initial set of seeds constraints the direction that the annotation can take and some parts of the sequences of the protein universe remain without annotation, particularly in non-model organisms, without being however true orphan sequences. Even well characterized organisms such as Homo sapiens or Escherichia coli have a part of their proteome in the dark  17  , i. e. protein sequences without annotations. For instance, Pfam-A coverage per residue is limited to only 45% of the human proteome even if a large portion of human proteins, 90%, has at least one Pfam annotation  17.
The originality of the present study is: (i) to enlarge the definition of the known universe by considering sequence-based domain databases (for the description of the gray regions, as introduced by Perdigão and colleagues  23  ) and (ii) to analyze part of these un-annotated sequences that correspond to foldable domains. These last domains are currently difficult to isolate in absence of homologous sequences, i. e. without the knowledge of domain models, a fact that explains their absence in domain databases. The detection of un-annotated, but foldable domains of the dark proteomes relies on the use of an automatic tool, called SEG-HCA, derived from the Hydrophobic Cluster Analysis (HCA) methodology. It allowed to identify, in a comprehensive way, foldable domains from the only information of a single amino acid sequence, without the prior knowledge of homologous sequences  14.
The principle of this approach, whose reliability has been supported by confrontation to both structure and domain databases  14  , is to delineate regions characterized by a high density in hydrophobic clusters, which mainly correspond to regular secondary structures that form globular domains  24, 25. The properties common to these un-annotated foldable domains were then analyzed, in order to understand what let them in the dark. To this purpose, the un-annotated foldable domains were compared to known domains annotated using two distinct methodologies, considering domains with structural homologs detected by mapping of the Protein Data Bank (PDB)  26  as well as protein domain families stored in databases. As in the work of Perdigão et al. 23  , domain annotations were performed on the Uniprot/Swissprot dataset  27.
The quality of the sequences from this dataset has indeed the advantage to strongly limit the potential bias from artefacts coming from genome annotation assembly or prediction errors, in addition to provide a balanced set of proteins from different organisms, thus avoiding over-representation of some taxa. This high quality dataset thus supports the relevance of the differences observed between un-annotated foldable domains and known domains stored in databases. Some differences are striking, in particular at the level of the hydrophobic amino acids topology. Finally, several remote homology detection tools were used to estimate the proportion of the un-annotated domains of the dark proteome that can be related to known folds and highlight those that may correspond to true novelties. Interestingly, the less information there is for an un-annotated domain the more important is the difference at the level of the hydrophobic amino acid topology relative to known domain families.
Results The dark proteome, and the importance of the methodology used for annotation. Starting from an initial set of 549 832 proteins extracted from Uniprot/Swissprot, different methods of annotations were used and led to distinguish between annotated protein segments and protein segments or full protein sequences that weren't annotated. These last sequences correspond to the dark protein universe, i. e. the unknown parts of the protein universe, by reference to the works of Levitt 1 and Perdigão et al. 23. Why these sequences are un-annotated and to which kind of molecular units they correspond, if they do, are unanswered questions that could bring novel insights into our understanding of the evolutionary dynamics of the protein universe. In this study, four categories were used to distinguish different types of annotations: PDB regions (sequences for which experimental structural data are available), gray regions (automatic domain annotation), dark regions and dark proteins.
Dark regions are defined as residues that are not assigned to a PDB entry (Uniprot/PDB mapped regions) or to a protein domain model. A protein is considered as a dark protein if a single dark region covers the entire protein sequence. Gray regions include information coming not only from structural source (Protein Model Portal), as in the work of Perdigão et al. 23  , but also from sequence sources (Pfam and CDD HMMs). This should thus significantly extend the coverage of the protein universe by known domains. However, it is important to note in this context that the structural information (with or without PMP annotations) is well covered by domain profiles (93. 12 and 78. 86%, respectively), indicating that domain annotations well capture the key features of folded domains. In contrast, coverage of the gray regions by structural information (PMP and Uniprot/PDB mapped regions) is 64.
61%, B: 0. 33%, A: 1. 26%, V: 8. 74%). As in the study of Perdigão and colleagues  23  , a larger proportion of dark proteins is detected in the viral protein datasets than in the other three kingdoms. This is in agreement with the fact that viral proteins are known to be difficult to annotate, by contrast with the annotations found for bacterial and archaeal proteomes. The largest part of the amino acid sequences in the Uniprot database is made of gray regions, i. e. parts of protein sequences annotated by at least one domain model (E: 57. 23%, B: 92. 84%, A: 89. 46%, V: 54. 49%). The remaining sequences constitute the dark regions of the protein universe, i. e. un-annotated parts of proteins that have annotation elsewhere on their sequences (E: 37. 35%, B: 4. 87%, A: 4. 64%, V: 33. 33%).
The advantage of SEG-HCA in the characterization of the dark proteome is to allow the prediction of these foldable domains from the only information of a single amino acid sequence, without the prior knowledge of homologous sequences. In the remaining of the manuscript, references to sequences of the four groups (PDB regions, gray regions, dark regions and dark proteins) correspond to HCA domains of these groups, except if specified otherwise, as the goal of this study is to understand why these foldable domains are not annotated, in contrast to globular domains stored in domain databases. Short HCA domains, less 30 amino acids long (21. 8% of the total number of HCA domains), were also discarded to only keep HCA domains with a high trend toward foldability. The amino acid coverage by HCA domains is presented in Supporting Information Fig.
S5 ). However, the distributions of amino acids percentages considering all strong hydrophobic amino acids (V, I, L, F, M, Y, W; Fig. 1a ) show clear differences between the four groups of sequences (two sample Kolmogorov-Smirnov tests, p-values ≪ 0. 001), the dark proteins and dark regions being characterized, on average, by a lower content in strong hydrophobic amino acids but also a larger variance. Interestingly, sequences of dark regions and dark proteins also have more short and long hydrophobic clusters, respectively, than the other groups (Fig. S6 ), pointing out a different organization at the hydrophobicity/secondary structure level. The amount of predicted intrinsic disorder was calculated for the HCA domains of the four categories using the consensus data from the MobiDB database  34.
Differences are again observed for HCA domains of the dark proteins and dark regions, these having on average a higher content in predicted disorder (Fig. 1b ) (two sample Kolmogorov-Smirnov tests, p-values ≪ 0. 001). This trend is also observed for the prediction of regions undergoing folding upon binding by the ANCHOR program  35, 36  (Fig. S7a ) (p-values ≪ 0. 001). The percentage of amino acids in transmembrane regions was also calculated for the HCA domains belonging to the four different groups: PDB regions include 0. 58% of amino acids predicted in transmembrane segments, gray regions 3. 34%, dark regions 2. 01% and dark proteins 3. 59%. As expected, domains in PDB regions display a lower coverage. Interestingly, the percentage of residues belonging to transmembrane segments is very similar between HCA domains of gray regions and dark proteins.
Their percentages of residues annotated as part of transmembrane regions are the only ones above 3%. This indicates that the percentage of transmembrane regions from domain databases is similar to the percentages found in domains delineated by SEG-HCA. Further analyses were made by calculating the distances between hydrophobic clusters included in HCA domains of the four categories. The first distance, called d1, is the distance between the last residue of a hydrophobic cluster and the first residue of the following one, and the second distance, called d2, is the distance between the first residue of a first hydrophobic cluster and the last residue of the second one (see Fig. S13  in the Supporting Information). Figure  2  shows very similar distributions of distances for the sequences from the PDB and the gray regions.
Going further into the dark regions. To remove the potential noise in our characterization of HCA domains that should result from undetected similarities due to the limited sensitivity of domain database profiles, PSI-BLAST searches were first performed using HCA domains of the dark proteome as queries, against   V, I, L, F, M, Y, W ) and protein sequence energy (HCA domains of the protein universe). Frequency and energy were computed for the different groups of sequences. The energy corresponds to the folding energy computed using HP-models (HPstruct). The colormap corresponds to the probability density computed from a Gaussian kernel estimation over the data points. the sequences of the gray and PDB regions. Queries were then removed in case of an alignment with another sequence, with an E-value lower than 1e10 -3 and a coverage of the HSPs superior or equal to 50% of the query length.
From the initial 223912 HCA domains, 67684 were filtered out in this way. These 67684 HCA domains mainly correspond to domains from dark region sequences (97. 36%), the remaining ones (2. 64%) corresponding to domains from dark proteins. The degree of redundancy in HCA domains was then analyzed using psi-cd-hit, in order to cluster similar sequences of the dark proteome. The results, obtained by using a sequence identity threshold of 30%, are displayed in Table  1. Interestingly, 67217 out of the 156228 sequences (43. 02%) in the dark proteome dataset don't have any obvious similarity with any other sequence and 51972 others (33. 27%) are in clusters containing between 2 to 4 sequences. Moreover, 73. 71% of the clusters containing more than one protein are made of proteins with the same Uniprot family identifier, whereas only 18.
4  and show the corresponding HCA plots in Figs S9,10,11 and S12. Figure  S9  gives an example of a hidden BRCT domain. In this case, the dark HCA domain query sequence (fission yeast MDB1, Uniprot O14709, between amino acids 486 to 598) directly matched BRCT domains of targets from the uniprot_20 database, therefore, corresponding to the first group described above. The BRCT domain was easily recognized with its typical signature  40  and can be aligned with the second BRCT domain of the tandem repeat of MDC1, its human ortholog, which is a key factor in DNA damage response  41. Figure  S10  shows one of the 4413 dark HCA domain queries partially matching an annotated domain. The annotated domain corresponds to spectrin repeats (cd02488), which are small independent folding units forming triple coiled-coils  42.
Discussion The dark proteome constitutes a terra incognita of our understanding of the protein universe. It is a potential reservoir of new folds and new functions, and a challenging target for researchers whose goal is to elucidate the evolutionary relationships between actual protein sequences. Fully understanding these relationships will shed light on the evolutionary path leading to the current protein universe  1, 3, 44, 45. Furthermore, the annotation of protein sequences with the most complete list of domains as possible is now a necessary step in comparative analysis of species proteomes, as the gain, loss and expansion of protein domain families inform on the evolutionary history of the studied species  46, 47. In this study, the Uniprot/Swissprot sequence database was thoroughly analysed using various tools to annotate domains, in order to provide the deepest annotation coverage of protein sequences.