dataesr/scientific-paragraphs-categorization huggingface dataset:
Differential Wt1 phenotypes correspond to different Wilms' tumour sub-groups. As the Nes-Cre and Pax8 +/Cre drivers result in Wt1 loss immediately before and after the nephron MET respectively, we reasoned that if disturbance of the MET is indeed important in the formation of Wilms' tumours  (Hastie, 1994) , characteristics of the tumours might already be found in these E18. 5 mutant kidneys. If so, it would suggest these changes are the direct effects of WT1 loss rather than events selected for during, or bystander effects of, the tumourigenic process. We compared a published microarray dataset of WT1-mutant and WT1wild-type tumours  (Corbin et al. , 2009)  to the Nes-Cre Wt1 conditional and Pax8 +/Cre Wt1 conditional datasets (Fig. 8A ; Supp. Table  3 ). For the decreased genes the biggest overlap was found in the intersection between the Nes-Cre Wt1 conditional and both sets of Wilms' tumour datasets (9 genes) or the Pax8 +/Cre Wt1 conditional and both tumour sets (18 genes).
Disease Models & Mechanisms DMM Accepted manuscript These genes were enriched for muscle-related GO-terms (Supp. table 3). The 'molecular function' significant hits included 'myosin binding' (p=3. 15E-6), 'troponin T binding' (p=2. 79E-6) and 'structural constituent of muscle' (p=3. 75E-4). Significant hits for the 'biological process' GO-term included only muscle-related terms, like 'muscle filament sliding' (p=1. 00E-13), muscle organ development (p=9. 59E-5) and 'muscle contraction' (p=2. 46E-8). Finally, the 'cellular component' GO-term also identified the 'muscle' theme with significant hits including 'sarcomere' (p=9. 30E-12), 'striated muscle thin filament' (p-=2. 46E-7) and 'troponin complex' (p=1. 23E-5). We analysed the increased gene sets in more detail at the functional level using ToppCluster for 'biological process' GO-terms.
Enriched terms were colour coded for different categories to enable the visual recognition of patterns in the datasets (Figure  8B ). In accordance with the comparison at the gene level, muscle-related terms (red nodes) are strongly clustering with the Nes-Cre Wt1 conditional and / or WT1-mutant Wilms' tumours samples. Within these datasets, a 'developmental cascade' can be recognized; terms uniquely enriched in the Nes-Cre Wt1 co/co samples are related to muscle cell proliferation and differentiation, in the overlap between the Nes-Cre Wt1 co/co and WT1-mutant tumours for filament assembly and uniquely in the WT1-mutant tumours for muscle function-related terms, such as contraction and relaxation, as well as differentiation into specific muscle types. GO-terms related to bone and cartilage processes, blue nodes, do not show such dataset-specific enrichment clustering.
Apoptosisrelated GO-term enrichment (yellow nodes) are spread over the four datasets as well, but there is a remarkable shift from enrichment of pro-apoptotic GO-terms for the two mouse samples to negative regulation of apoptosis in the tumour samples. Enrichment for epigenetic modifier functions (green nodes), especially H3K4 and H3K9 methylation, is uniquely found in the WT1 wild-type tumours. Cell cycle / cell division enrichment (orange nodes) is also biased towards the WT1 wild-type tumours, though not exclusively. Notably, whereas all samples show enrichment for some cell division-related GO-terms, the WT1 wild-type tumours show enrichment for regulation of every possible aspect of cell cycle control, including every phase transition, spindle functions, checkpoints, DNA replication and chromosome separation.
This comparison showed a close resemblance between the Nes-Cre Wt1 co mutants and the WT1mutant tumours, especially with respect to the ectopic muscle development signature, whereas the phenotype in the Pax8 +/Cre Wt1 co mutant kidneys more resembled the one found in WT1 wild-type Wilms' tumours. This would suggest the latter originates in a developmental block at the same stage as the Pax8 +/Cre -driven mutants even if the causative mutation is different and currently unknown. Whereas our data provides experimental support for the model put forward by  Fukuzawa et al (2009)  and  Gadd et al (2012)  on different developmental origins of these two groups of tumours, our data suggests different developmental stages than deduced by Gadd et al from the expression profile of the tumours. The authors suggested an intermediate mesoderm origin for the WT1-mutant tumours and a metanephric mesenchyme origin for the WT1 wild-type cases.
Disease Models & Mechanisms DMM Accepted manuscript developmental block  (Kreidberg et al. , 1993). We serendipitously found a single surviving Nes-Cre Wt1 co/co mouse that developed a stromal predominant Wilms' tumour at 5 months of age in the right kidney. As this mouse had a bigger than usual number of escaping nephrons in the left kidney, we assume this kidney kept the mouse alive to allow the development of the further tumour from the right kidney. We do not present this single case as a mouse model for  Wilms'  tumours, but it does confirm that an MET block can give rise to these tumours. Equally, the post-MET / early tubulogenesis block found in the Pax8 +/Cre Wt1 co kidneys more resembles the epithelial nature of WT1 wild-type tumours. So although order of developmental blocks in our data is the same as proposed by Gadd et al, the exact stages have shifted.
Alternatively, the dedifferentiation of the Wilms' tumour CSCs as described by  Shukrun et al (2014)  could play a role in the ectopic differentiation found in these tumours. Either way, better understanding of this group of tumours could provide valuable new insights in the link between stage of origin and Wilms' tumour phenotype. If our interpretation of the Nes-Cre and Pax8 +/Cre driven Wt1 mutants and how they compare to WT1-mutant and WT1 wild-type tumours is correct, our data also highlights other differences between these two tumour groups. Most remarkable is the presence of classic Disease Models & Mechanisms DMM Accepted manuscript cancer hallmark GO terms in the WT1 wild-type tumour / Pax8 +/Cre Wt1 conditional sets but absence of these in the Nes-Cre driven mutants / WT1-mutant tumours.
Immunohistochemistry. Embryos were taken at E18. 5 and fixed in freshly prepared 4% paraformaldehyde overnight at 4ºC. Following fixation the embryos were paraffin embedded, sectioned at 7μm and stained with haematoxylin / eosin. Alcian Blue and Alizarin Red staining was done as described elsewhere  (Bancroft and Stevens, 1990). The measurements were taken every 10 hours using IPLAB image analysis software (Scanalytics, MD, USA) on the still images obtained from the time-lapse movies. In order to synchronize the measurements between different kidneys and between different branches, the moment that the branch of interest bifurcated was redefined as time 0.
Acknowledgements. The authors like to thank  Lee Murphy ,  Angie Fawkes  and  Louise Evenden  at the  Edinburgh Wellcome Trust Clinical Research Facility  for microarray analysis,  Dirk-Jan Kleinjan  for supplying the germline-Cre delete mouse,  Allyson Ross  for help with histology,  Harris Morrison  for help with OPT and  Matthew Pearson  for help with imaging.
Data was analysed using Genespring. All samples were analysed in biological triplicates except for Nes-Cre Wt1 co/co where one samples failed the analysis QC. Overlaps in differentially expressed genes between different mutants were identified using Venny (http://bioinfogp. cnb. csic. es/tools/venny/index. html). Geneset enrichment analysis was done using ToppGene (http://toppgene. cchmc. org/) and ToppCluster (http://toppcluster. cchmc. org/). Networks were manually ordered, categorized and coloured in Cytoscape 3. 02  (Smoot et al. , 2011).
Background: Changes to mRNA lifetime adjust mRNA concentration, facilitating the adaptation of growth rate to changes in growth conditions. However, the mechanisms regulating mRNA lifetime are poorly understood at the genome-wide scale and have not been investigated in bacteria growing at different rates. Results: We used linear covariance models and the best model selected according to the Akaike information criterion to identify and rank intrinsic and extrinsic general transcript parameters correlated with mRNA lifetime, using mRNA half-life datasets for E. coli, obtained at four growth rates. The principal parameter correlated with mRNA stability was mRNA concentration, the mRNAs most concentrated in the cells being the least stable. However, sequence-related features (codon adaptation index (CAI), ORF length, GC content, polycistronic mRNA), gene function and essentiality also affected mRNA lifetime at all growth rates.
The binding of ribosomes (ribonucleoprotein complexes) to mRNA has been reported to have a dual role, either preventing or accelerating bacterial mRNA degradation  [5, 6]. These studies directly highlighted the role played by mRNA sequence-related determinants, such as specific recognition sequences  [7]  and secondary structures  [8] , in the mechanisms regulating mRNA lifetime. These mechanisms have been studied and described for particular transcripts and may be specific to the transcripts concerned. However, the global mechanisms determining mRNA stability patterns relevant for the genomewide regulation of mRNA levels have yet to be studied and characterized. The large-scale datasets already available for mRNA half-life and the development of "omics" methods have opened up new possibilities for discovering and characterizing global regulation patterns and their underlying mechanisms.
We carried out a genome-wide analysis of transcript lifetimes in the model bacterium, E. coli MG1655, at various growth rates. We used the largest (70% of E. coli genes) and the most recent mRNA half-life datasets (determined by transcriptomic-based methods) available for this microorganism  [1]. The half-lives ranged from 1 to 53 minutes and were shown to be dependent on growth rate  [1]. However, the key factors responsible for this considerable variability of mRNA stability remain unknown. We investigated the major parameters correlated with mRNA stability, by modeling all mRNA half-lives statistically, taking into account, without the a priori selection of parameters, both qualitative (e. g. gene function, gene essentiality) and quantitative (e. g. mRNA concentration, codon adaptation index (CAI, a measure of synonymous codon usage bias  [18] )) biological factors.
This made it possible to rank the factors most strongly influencing growth rate-dependent mRNA stability patterns. We found that the general parameters explained more than half the variability of mRNA stability, even in the presence of mRNA-specific regulation. The nature of the general parameters identified provides valuable insight into the genome-wide mechanisms associated with mRNA stability. For example, mRNA concentration, length and GC content are directly related to the activity of the degradation machinery; CAI and a sequence motif are related to the translational activity of ribosomes and other parameters (e. g. gene function) are related to as yet unknown mechanisms. Finally, we also investigated the role of mRNA stability regulation in determining growth rate-dependent protein levels in cells, using new proteomic datasets for E.
Results We used a linear covariance model to identify factors relating to mRNA stability in E. coli. This analysis was based on large-scale mRNA half-life datasets  [1] , taking into account both quantitative (e. g. mRNA concentration, length, sequence composition and structure, CAI) and qualitative (e. g. gene essentiality, gene function, cellular distribution of the gene product, predicted peptide signal, polycistronic transcript) parameters (the complete list is provided in the Methods section). A previous study reported differences in the patterns of mRNA stability regulation between growth rates  [1]. We therefore decided to use four linear covariance models corresponding to different growth rates (μ = 0. 10, 0. 20, 0. 40 and 0. 63 h -1 ). For each model, 1589 half-life values (corresponding to ~37% of all E.
coli coding genes) were available: these datasets for mRNA half-life are the largest ever used to identify mRNA stability determinants. The most important parameters, selected on the basis of the Akaike information criterion (AIC, see the Methods section), are listed for each model in Table  1. This subset of parameters included both quantitative and qualitative parameters, reflecting intrinsic features (e. g. CAI, length, GC%, function) and extrinsic parameters dependent on the environment (e. g. [mRNA], essentiality of the gene function). The selection of these parameters resulted in a relatively good quality of fit for all four models, with determination coefficients (adjusted R 2 ) of between 0. 74 at μ = 0. 10 h -1 and 0. 51 at μ = 0. 63 h -1. These results indicate that mRNA stability is well accounted for by the selected parameters, particularly at the lowest growth rates (see the Discussion).
this parameter was selected on the basis of the AIC at μ = 0. 10 h -1 and μ = 0. 63 h -1 (Table  2 ). The estimated coefficients were significant but small, with opposite signs at the two growth rates (-0. 14, and +0. 08 at μ = 0. 10 and 0. 63 h -1 , respectively). These data suggest that the weak correlation between mRNA half-life and protein level may be dependent on growth rate. The low impact of mRNA stability on protein level and the sign inversion at high growth rate were quite surprising because (i) protein level and mRNA concentration were strongly positively correlated for 643 mRNA and protein pairs in this dataset (estimated model coefficient > 0. 50, P-value < 10 -55 ) and (ii) mRNA concentration and half-life were strongly negatively correlated (as demonstrated above in the mRNA half-life determination models for a population of 1589 mRNAs).
This intriguing finding may be accounted for by the 643 mRNAs used in the protein determination models being not entirely representative of the larger set of 1589 mRNAs used in the mRNA half-life determination models, as shown by the non-overlapping densities of mRNA levels for the two datasets (Additional file 3: Figure  S2 ). Nevertheless, our models confirmed the significant positive effect of CAI on protein level (estimated coefficient > 0. 48, P-value < 10 -31 ) (Table  2 )  [27]. Indeed, CAI, which is related to translation efficiency, has frequently been shown to have a strong positive influence on protein concentration  [19] [20] [21] [22].
Discussion We used the largest and most recent mRNA half-life datasets for E. coli and a large number of parameters in linear covariance models, to identify and rank the general parameters associated with mRNA stability at four different growth rates. The determination coefficients (R 2 ) of the four models corresponding to the different growth rates were high (>0. 51), indicating that a large proportion of mRNA stability could be explained by a combination of the selected general parameters. This finding highlights the importance of general mechanisms for the regulation of mRNA stability. The general.
Data compilation We obtained mRNA half-life and expression data for 2947 genes, for E. coli growing at four different rates (μ = 0. 10, 0. 20, 0. 40 and 0. 63 h -1 ), from a previous study  [1]. ORF lengths and sequences were retrieved from the Ecocyc database (http://www. ecocyc. org/). The 5′UTR lengths and sequences of all the mRNAs considered were experimentally determined in a previous study  [38]. If several alternative transcription start sites (TSS) had been identified for a transcript, we used the TSS furthest from the site of translation initiation in the analysis, to minimize the loss of sequence information. The second nucleotide (purine/pyrimidine) of each mRNA was identified for these 5′UTR sequences. We obtained 5′ UTR + ORF sequences and lengths by combining the results for the 5′UTR and ORF described above.
We retrieved transcription units from the RegulonDB database (http://regulondb. ccg. unam. mx/), to determine the number of genes in operons and their positions within the polycistronic mRNA. Clusters of orthologous groups of protein (COG) annotations and the cellular distributions of gene products were obtained from the E. coli K-12 annotation  [39]. Essential genes were identified by determining knockout efficiency, as previously described  [23]. The compiled data are presented in Additional file 4: Table  S2.
Sequence-related features of mRNA and proteins The GC contents of the 5′UTR and ORF were calculated as the percentage of G and C nucleotides in each sequence. Peptide signals in ORF sequences were predicted with SignalP4. 1 software (http://www. cbs. dtu. dk/ services/SignalP/). The codon adaptation index (CAI) of the ORF was calculated as previously described  [18] , with 55 ribosomal proteins used as a reference. We calculated the Z score as a measurement of thermodynamic stability for the -30 to +24 bp region of each mRNA relative to its start codon. The Z score was defined for each region as the minimum free folding energy of the native sequence minus the mean minimum free folding energy of its randomized sequences divided by the standard deviation of the minimum free folding energy of its randomized sequences.
Randomized sequences were computed by shuffling each region of interest 100 times, with Ushuffle software  [40]  which conserves dinucleotide composition. The minimum free folding energy of native and randomized sequences was estimated with the RNAfold module of the Vienna Package  [41]. The grand average of hydropathicity (GRAVY) score for the linear polypeptide sequence was calculated as the sum of hydropathy values for all amino acids, divided by the number of residues in the sequence  [26].
Search for sequence motifs We included in the analysis the 1937 5′UTR sequences experimentally determined  [38]  of mRNAs with a measured half-life value (this study). When the 5′UTR sequence was longer than 100 nucleotides, we limited the motif search to the 100-nucleotide region immediately upstream from the initiation codon. We searched for stabilizing and destabilizing sequence motifs in the top (most stable) and bottom (least stable) quartiles of the mRNA half-life distribution. Eight datasets were constructed, each containing the 485 most or least stable mRNAs, separately, for μ = 0. 10, 0. 20, 0. 40 and 0. 63 h -1. We used RMES software (http://migale. jouy. inra. fr/ ?q=rmes,  [24] ) to search for differences between unstable and stable mRNAs in terms of the motifs present in the 5′UTR sequences for these subgroups.
Gut microbiota has been implicated as a pivotal contributing factor in diet-related obesity; however, its role in development of disease phenotypes in human genetic obesity such as Prader-Willi syndrome (PWS) remains elusive. In this hospitalized intervention trial with PWS (n = 17) and simple obesity (n = 21) children, a diet rich in non-digestible carbohydrates induced significant weight loss and concomitant structural changes of the gut microbiota together with reduction of serum antigen load and alleviation of inflammation. Co-abundance network analysis of 161 prevalent bacterial draft genomes assembled directly from metagenomic datasets showed relative increase of functional genome groups for acetate production from carbohydrates fermentation. NMR-based metabolomic profiling of urine showed diet-induced overall changes of host metabotypes and identified significantly reduced trimethylamine N-oxide and indoxyl sulfate, host-bacteria co-metabolites known to induce metabolic deteriorations.
Metabolomic Analysis The fecal metabolite extraction  (Wu et al. , 2010) , and the urine sample preparation  (Jiang et al. , 2012; Xiao et al. , 2009)  were performed as described previously. All one-dimensional (1D) 1 H NMR spectra of fecal water and urine samples were acquired on a Bruker AVIII 600 MHz NMR spectrometer equipped with a cryogenic probe (Bruker Biospin, Germany). The first increment of NOESY pulse sequence was employed with continuouswave irradiation on water peak during recycle delay and mixing time for water suppression. The 90°pulse was adjusted to about 10 μs, and 64 scans were collected into 32 k data points for each spectrum with the spectral width of 20 ppm. To assist metabolite assignments, twodimensional (2D) NMR spectra were acquired including 1 H-1 H COSY, 1 H-1 H TOCSY, 1 H J-resolved, 1 H-13 C HSQC and 1 H-13 C HMBC for typical samples.
454 Pyrosequencing DNA extraction from fecal samples was conducted as previously described  (Godon et al. , 1997)  and purified by QIAamp DNA mini kit (51304, QIAGEN, Germany). The primers 5′-CGTATCGCCTCCCTCGCG CCATCAGACGAGTGCGTAGAGTTTGATYMTGGCTCAG-3′ and 5′-CTATGC GCCTTGCCAGCCCGCTCAGNNNNNNNNNNATTACCGCGGCTGCTGG-3′, with a sample-unique DNA barcode of ten-mer sequences represented as N, were used to amplify the V1-V3 region of each fecal sample by PCR as previously described  (Zhang et al. , 2012). PCR products were mixed at equal ratio for pyrosequencing using the GS FLX platform (Roche, Branford, CT, USA). The details of sequencing data analysis were shown in Supplementary Materials.
Co-Abundance Gene Groups (CAGs) All the genes were clustered into CAGs based on their abundance data using the Canopy-based algorithm with default parameters. CAGs with more than 700 genes were regarded as bacterial CAGs for further analysis. CAG abundance profiles were calculated as the sample-wise median gene abundance  (Nielsen et al. , 2014). The PCoA of bacteria CAGs based on different distance and MANOVA was performed using QIIME. Procrustes superimposition was then performed from the PCoA of CAGs and principal component analysis (PCA) of bioclinical data.
Comparison of Assemblies and Reference Genomes 118 high quality assemblies passing 5 or 6 HMP criteria for reference genomes were compared to 352 draft reference genomes from the human gastrointestinal tract HMP DACC database and 2645 complete reference genomes from the NCBI database (downloaded on May 6, 2014). The comparison was performed with MUMmer3. 0  (Kurtz et al. , 2004). The contigs were aligned to the reference genomes with numcer (default parameters). Delta-filter (parameters: -i 95 -o 80) was used to filter the low identity matches. Finally, the comparison of the assemblies and the reference genomes and the estimation of mean identity and coverage were performed with dnadiff.
Phylogeny of the Bacterial CAG Assemblies We used CVtree3. 0 web server  (Xu and Hao, 2009) , which applies a composition vector to perform phylogenetic analysis, to construct a phylogenetic tree of the 118 bacterial CAGs with high quality assemblies, 352 reference gastrointestinal tract genomes from the HMP DACC database, and the server's inbuilt database, to identify the phylogenetic position of the bacterial CAGs. We also applied SpecI  (Mende et al. , 2013) , which is a method to group organisms into species clusters based on 40 universal and single-copy phylogenetic marker genes, to delineate the bacterial CAGs. 64 bacterial CAGs could be delineated by SpecI, and only 5 of the 64 showed disagreement with CVtree (CAG00178 Streptococcus gallolyticus (SpecI), Streptococcus pasteurianus (CVtree); CAG00047 Ruminococcus gnavus (SpecI), Lachnospiraceae bacterium 2 1 58FAA (CVtree); CAG00120 Ruminococcus gnavus (SpecI), Lachnospiraceae bacterium (CVtree); CAG00226 Parasutterella excrementihominis (SpecI), Burkholderiales bacterium 1_1 _47 (CVtree); CAG00020 Bacteroides uniformis (SpecI), Bacteroides sp.
Metagenomic CAZy Profiling A local-version database was downloaded from the dbCAN website (http://csbl. bmb. uga. edu/dbCAN/annotate. php, version: 11 May, 2013). Genes in the non-redundant gene catalogue were aligned to the database with HMMscan  (Finn et al. , 2011). The alignment result was parsed by the hmmscan-parser. sh, which was provided by dbCAN, and the best-hit alignment was retained. The CAZy family profile was calculated by summing the relative abundance of genes aligned to it.