dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We would like to thank  Keith Burling  of the  Core Biochemical Assay Laboratory, Cambridge , and  Tertius Hough , Pathology Operations Manager at  Medical Research Council Harwell Institute , for analysing the plasma and urine samples. We are also grateful to the  Pathology Facility  and the  Light Microscopy Facility of the Barts Cancer Institute  for their help with histology and imaging. All acknowledged authors have provided permission to be named in the manuscript. Sources of funding: This work was supported by  Barts Charity  grant  MGU0361  to  JL Williams , the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  (FP7/ 2007-2013) under Rea grant agreement  608765  to  S Awad , the  Medical Research Council  grant  165071  to  J Nicholson , by a  Marie Sklodowska Curie Innovative Training Network programme award  ( 721532 ) to  D Grzesik , a  Medical Research Council  grant  MR/R02426X/1  to  J Botta , a  British Heart Foundation  grant  RG/15/15/31742  to  A Tinker , and  Medical Research Council  grant  MC_U142661184  to  RD Cox.
In this work we develop an efficient method for inferring both actual and intrinsic codon-specific elongation rates from the ribosome profiling data based on the mathematical solution of the TASEP that we recently developed  12, 27. Using the TASEP, we argue that the ribosome density alone is not sufficient to estimate the absolute elongation rates from the ribosome profiling data. Instead, our method infers elongation rates of an mRNA relative to the initiation rate of that transcript. Moreover, we propose new measures of translation efficiency that quantify the amount of ribosome traffic around the START codon and along the transcript. We apply our method to several Ribo-seq datasets in Saccharomyces cerevisiae and show evidence of local queuing in vivo.
Ribosome profiling data We have analysed publicly available ribosome profiling data of Saccharomyces cerevisiae from Guydosh et al. 14  , Pop et al. 20  and Weinberg et al. 23  : NCBI GEO accession numbers GSM1279568, GSM1557447 and GSE75897 respectively. We downloaded the HDF5 files from Riboviz (https://riboviz. org)  28 and mapped to A-site positions according the following table  29   After obtaining the A-site read density profiles, our method successfully optimised 345 of the total 346 genes from the Guydosh dataset for which the experimental ribosome density necessary for normalisation was known from MacKay et al. 30  Analogously, the optimisation was successful for 1051 out 1053 genes of the Pop dataset and for all 1589 genes in the Weinberg dataset. For the omitted genes the normalisation was not possible because it resulted in ribosome density larger than 1.
However, we emphasise that the initiation-limited approximation does not assume that ribosome collisions are absent. Instead, our analytic solution takes ribosome collisions into account and is applicable to a wide range of initiation rates as long as they are smaller than the elongation and termination rates (see Supplementary Information). We have applied our method to ribosome profiling data of Saccharomyces cerevisiae obtained by Guydosh et al. 14  , Pop et al. 20  and Weinberg et al. 23. These datasets were selected for their lack of using cycloheximide to inhibit translation elongation, which is known to distort ribosome coverage profiles  43, 44. The raw data was processed by the Riboviz software  28  and mapped to A-site positions following the table provided in Ahmed et al. 29. After obtaining the A-site read density profiles the method proceeded in four steps, which we summarise below.
Using NEAR to study translation of individual genes We demonstrate our method on a particular gene (YLR301W) using Ribo-seq data from the Weinberg dataset  23. We first compute the normalised experimental density profile {r i } using the experimental absolute density r from MacKay et al. 30  (in units of ribosomes/codon). This profile is then analysed following the method explained in the previous section. A set of elongation-to-initiation ratios {κ i } is obtained by optimising the match between the model-predicted density profile and the experimental one. Each κ i is then examined to see whether it provides a good prediction for that particular codon position and to check for inconsistencies in the method as previously explained. There are few values of κ i that do not pass this quality check, which are rejected and are not included in the final analysis.
Estimate of elongation-to-initiation ratios at codon resolution in yeast We analysed three different datasets  14, 20, 23  and gathered the NEAR elongation-to-initiation ratios {κ i } for each gene. The percentage of codons that passed the quality check (points 4(a) and (b)) for the Weinberg, Pop and Guydosh datasets is 75%, 66% and 44%, respectively. These are the percentages of the total number of analysed codons, i. e. without taking into account different transcript lengths. We also computed the percentage of rejected codons for each transcript. The percentages of codons that were rejected at point 4(a) have a median value of 2. 3% (Weinberg), 3. 8% (Pop) and 8. 2% (Guydosh). The respective medians for the percentages of codons that passed 4(a) but were rejected at point 4(b) are 9. 5%, 16% and 26.
7%. Again, the best fit is achieved for the Weinberg dataset. We note that our analysis included only transcripts with large number of reads per codon (10 or more), i. e. with high ribosome traffic. If we had analysed all transcripts, the percentage of accepted codons would have been higher. However, many transcripts with low read count have codons with zero reads, which are difficult to handle in the model as they imply unphysically large elongation speed. We now turn to the codons that passed the quality check. The estimated elongation-to-initiation ratios passing the quality check are plotted in Fig. 4  and compared to the naive estimates 1/r i. In particular, we find many instances where 1/r i deviates from κ i obtained by NEAR. The model predicts that κ i ≈ 1/r i if there are very few ribosomes on the transcript so that ribosome collisions are rare.
Our findings in Fig. 4  thus suggest that the effect of ribosome interference is not negligible. We will discuss this point later when we introduce better measures for detecting ribosome interference. Next, we wanted to understand if each codon type has a characteristic decoding time and verify or reject a common hypothesis that elongation rates are determined by the availability of aminoacyl-tRNAs. By definition, κ i is equal to the ratio k i /α between the elongation rate of codon i and the initiation rate α of the gene. Because the initiation rates are likely to be gene-specific, we do not know if the observed variation in elongation-to-initiation ratios of the same codon types (see Figure  S4 ) is due to variation in the elongation or initiation rates. However, we observe that STOP codons show the least variability of all the elongation-to-initiation ratios {κ i } in the Guydosh and Weinberg datasets (Figure  S4 ).
This result is consistent with the expectation of a context-independent termination. Thanks to this observation, we then compute the elongation-to-termination ratio κ i /κ L = k i /β , i. e. the elongation rate of codon i with respect to the termination of the gene under investigation (Figure  S5 ). This quantity does not depend on the initiation rate α that is likely to be context-dependent and different from gene to gene. Indeed, the variation in κ i /κ L linked to the same codon type is now more uniform across 61 codon types, especially in the Guydosh dataset (Fig. S5 ). We have also compared median values of κ i /κ L for each codon type against two common measures of tRNA availability: a codon-dependent rate of translation based on the tRNA gene copy number (GCN) corrected for the wobble base pairing from Weinberg et al.
Therefore we propose to use J/α as a measure of the Translation Initiation Efficiency (TIE), which takes values between 0 and 1. The TIE would be equal to 1 in the optimal case in which initiation is not hindered by ribosome traffic (J = α, hence TIE = 1). Otherwise, the TIE gives the probability that the first codons, potentially interfering with ribosome recruitment and initiation, are unoccupied. A TIE smaller than 0. 5 means that more than half of the times a new ribosome tries to initiate, it fails because of another ribosome whose A-site is located within the first ten codons. In Fig. 5  we plot the histograms of TIE for all the genes and datasets included in our study. We find that almost all genes show TIE > 0. 5 with a median value around 0. 8 for all the datasets. These values suggest that the first codons are mainly free from ribosomes that are already engaged in translation.
After analysing TEE profiles of all genes included in our study, we observe that the distribution of TEE i for all the codons that passed the NEAR quality check is peaked at 1, with the median at about 0. 99, as shown in Fig. 6(e ). This result is consistently found in all three datasets that we analysed, suggesting that ribosome interference is present only locally on a few codons, and is generally absent in vivo. If the ribosome density on a given transcript is high, one would expect to see an increased number of ribosomal collisions resulting in the TEE profile that clearly deviates from 1. In Figure  7  we present the mean of the TEE profile for each gene that we analysed compared to the ratio of the ribosome density for that gene and the maximum achievable density ρ max = 1/ , where ≈ 10 codons is the ribosome footprint length.
The results across all three Ribo-seq datasets clearly show that genes with low ribosome density have the mean TEE very close to 1 (few collisions). On the other hand, the mean TEE of genes with high ribosome density deviates significantly from 1 (many collisions). Another way to demonstrate the importance of ribosome collisions is to directly estimate t i (collisions). Since the total time spent on a codon is given by r i divided by the ribosomal current, we obtain α t i (collisions) = r i TIE - 1 κ i. ( 6 ) 10/28 The time spent in traffic on codon i is then larger than zero if r i /TIE > 1/κ i and equal to zero only if there is no ribosome interference. In the second row of Figure  7  we show that many of the codons analysed deviate from the bisect. This is another quantitative evidence that, according to experimental data, it is not that rare to observe ribosomes queuing in vivo.
To this end we introduce new measures of translation efficiency that we named translation initiation and elongation efficiencies (TIE and TEE, respectively). Importantly, both TIE and TEE are dimensionless scores taking values between 0 and 1, which allows us to compare ribosome traffic between different genes. Translation initiation efficiency (TIE) is defined as the probability that a ribosome attempting to initiate translation is not obstructed by another ribosome on the coding sequence. The distribution of TIE for the three datasets that we used in this study show that ribosomes can easily access most transcripts, with the median value of 0. 8 for the probability to find the initiation region unobstructed (Figure  6 ). Yet, we find genes with low TIE suggesting that the first codons can exert control over protein synthesis through ribosome traffic interfering with translation initiation.
Problematic codons. If |r i -ρ sim i ({κ i })|/r i > ε EXP then the codon is excluded from the final analysis and we set κ i = -1 (to identify the problematic codon for further analysis). Those codons fall in the most interesting class, in which the experimental data cannot be reproduced using the existing theory. We speculate that those "problematic" codons might also arise because ribosome profiling cannot detect clusters of ribosomes that will instead be predicted by our simulated profile {ρ sim i ({κ i })}. The fraction of problematic codons for each of the three datasets is presented in Figure  S1. A full list of codons entering in this category (in which our method can be applied but experimental data are inconsistent with the density generated by NEAR) is in the Supplementary Files 1,2,3.
e. for which κ i is fixed to -2 in the algorithm. Pink points are the ones that did not pass point 4(b), and for which κ i is arbitrarily fixed to be -1. Since NEAR is based on a power series approximation that is reliable for small initiation rates, the method is expected to perform badly for large values of α. However, even in this regime the quality check is able to exclude codons that will not be considered in the final analysis. We remind that initiation has been estimated to be limiting and the physiological situation is the one presented in panel (b) for most of the genes  37. For instance in panel (d) one would measure values of κ i as small as ∼ 2. 5, while the values of κ i estimated from datasets are generally at least on order of magnitude larger (see Figure  S4 ).
Weinberg Figure  S4. Distributions of elongation-to-initiation ratios κ for all codon types. We gather the ratios κ i = k i /α for each codon-type, and plot their distributions. In principle codons from different genes cannot be compared because they have a different initiation rate α. However, we notice a small variability in the estimates of STOP codons (TAA, TAG, TAA) for the Guydosh and Weinberg datasets. This is reasonable since STOP codons are supposed to be less context dependent. We also remark that, as opposed to what is generally believed, STOP codons are the slowest, but still ∼ 10 times faster compared to initiation. The boxplots show the quartiles Q1 and Q3 (∆Q = Q3 -Q1) of the distribution, the median is the orange horizontal line; the whiskers extend to the most extreme, non-outlier data points (i.
e. to the last data point less than Q3 + 1. 5∆Q and greater than Q1 -1. 5∆Q). The Guydosh dataset is the smallest one, and only 10 CGA codons passed the quality check. There is a ≈5-fold difference in the median value of κ/κ L between the slowest and the fastest codon, in accordance with a common view that translation elongation is a non-uniform process. However, in we also find a large variability in the values of κ/κ L belonging to the same codon type. This variability suggests that the elongation speed of individual codons is only partially determined by their codon type. The boxplots show the quartiles Q1 and Q3 (∆Q = Q3 -Q1) of the distribution, the median is the orange horizontal line; the whiskers extend to the most extreme, non-outlier data points (i. e. to the last data point less than Q3 + 1.
5∆Q and greater than Q1 -1. 5∆Q). Only 11 CGG codons of the Guydosh dataset can be used to plot these distributions (both CGG and STOP codon passed the quality check). When the TEE is low, many κ i do not pass the quality check, as it can be seen from the many points missing in the TEE profiles. This means that the experimental read counts are inconsistent with the model. We speculate that this is due to the bias in the ribosome profiling neglecting clusters of ribosomes. However, NEAR fills the partial that is enclosed in the experimental profile and finds evidence of traffic (small TEE).
, 2013). MFM-filaminopathy is associated with the expression of a toxic protein and not with reduced expression of FLNC. Here, for the first time, we describe a homozygous FLNC mutation leading to the manifestation of a severe myopathic phenotype. A combination of morphological, functional, and biochemical studies was performed to demonstrate pathogenicity. As the parents are asymptomatic and there is no family history of muscle disease, our data suggest that this novel c. 1325C>G (p. Pro442Arg) mutation is the first recessive mutation to be described for FLNC.
| Genetic analysis Exome sequencing and data analysis was performed as part of the MYO-SEQ project  (Töpf et al. , 2020). Sanger sequencing was performed in DNA to confirm the detected variants. Annotation of the FLNC gene is according to GenBank NC_000007. 14 and transcript ENST00000325888. The c. 1325C>G; p. Pro442Arg variant in FLNC, which we detected in our patient, is not observed in any current database of human genetic variations including the ExAC (http://exac. broadinstitute. org) and gnomAD (https://gnomad. broadinstitute. org) databases. In silico prediction modeling using different prediction tools lead to consistent variant classification of damaging (SIFT; https://sift. bii. a-star. edu. sg), possibly damaging (PolyPhen-2; http://genetics. bwh. harvard. edu/pph2) and disease-causing (MutationTaster; http://www.
| DISCUSSION We have for the first time identified a homozygous missense mutation in the FLNC gene in our pediatric patient that results in an amino acid exchange of proline to arginine. This mutation was not found in several genetic databases and control populations (dbSNP, ESP, ExAC, and gnomAD) or as a causal mutation in the literature. Algorithms to predict the deleteriousness of this amino acid exchange classify this variant as probably pathogenic, and within the 0. 3% most damaging variants of the genome. Mechanical instability of mutant FLNC and associated reduced strain resistance of myofibrillar Z-discs are well-known major pathophysiological cascades in MFM-filaminopathy, first identified in the most prevalent human FLNC mutation p. 2710X and the corresponding heterozygous mouse knock-in model  (Chevessier et al.
ACKNOWLEDGMENTS We thank  Hannelore Mader ,  Claudia Krude ,  Ronja Kardinal ,  Karin Bois ,  Lisa Wadephul ,  Swantje Hertel , and  Nancy Meyer  for their expert technical assistance. A. R. acknowledges the support from the  AFM  (Grant  21644 ). A. R. , U. S. , and A. S. also acknowledge funding in the framework of the  NME-GPS  project by the  European Regional Development Fund (ERDF). Moreover, L. K. and A. S. acknowledge the support by the  Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen , the  Regierende Bürgermeister von Berlin-inkl. Wissenschaft und Forschung , and the  Bundesministerium für Bildung und Forschung. This study was supported by the  German Research Foundation  (Multilocation  DFG-Research Units FOR1352  and  FOR2743 , D. O. F. ); MYO-SEQ was funded by  Sanofi Genzyme ,  Ultragenyx ,  LGMD2I Research Fund ,  Samantha J Brazzo Foundation ,  LGMD2D Foundation  and  Kurt+Peter Foundation ,  Muscular Dystrophy UK , and Coalition to Cure Calpain-3.
Based on these encouraging results, an inflammation score was calculated for each sample in the Colonomics dataset  (Figure 1A)  and in an extended dataset comprising 566 human CRC samples (hereafter GEO: GSE39582; Figure  1B ). A highly significant positive correlation between inflammation score and GZMA expression existed in both datasets. By contrast, no correlation was observed between inflammatory score and GZMB expression. To evaluate whether correlation between GZMA expression and inflammation was restricted to a specific CMS, the analyses were repeated and the samples were stratified by CMS status in both datasets (Figures  1A  and 1B , middle and lower panels). Significant correlation was observed in all CMS groups in both datasets, with the exception of CMS1 in Colonomics, probably because of the low number of samples.
The differences between WT and GzmA KO animals were statistically significant in all cases (Figure  3D ). Strong Ki-67, vimentin, and Male C57BL/6 WT mice were treated with DSS and DSS/AOM, as described in Method Details, and sacrificed at day 56. (A) mRNA levels of GZMA were determined by qRT-PCR (normalized to the amount of HPRT mRNA). The relative gene expression levels were expressed as the fold difference (2 ÀDDCt ), as described in Method Details. Data are presented as mean ± SEM from 3 biological replicates. Statistical analyses were performed by one-way ANOVA test with Bonferroni's post-test. *p < 0. 05; ***p < 0. 001. (B) Colonic tissue samples (30 mg) were cultured during 24 h, supernatant was collected, and GzmA was quantified by ELISA in proximal and distal colon. (C) GzmA enzymatic activity was determined.
Specific activity was calculated by subtracting the residual unspecific tryptase-like activity detected in GzmA KO animals used as control from the activity observed in WT mice. Data are presented as mean ± SEM from 5 biological replicates performed in 3 independent experiments. Statistical analyses were performed by one-way ANOVA test with Bonferroni's post-test. *p < 0. 05; ***p < 0. 001. (D) IELs were isolated from WT and GzmA KO mice at day 49 of treatment with DSS/AOM, as indicated in Method Details, and stained with a combination of anti-CD3-FITC, anti-CD8a-APC, anti-NK1. 1-APC-Vio770, and anti-CD45-VioBlue antibodies. Intracellular GzmA expression was analyzed using anti-GzmA-PE antibody. A representative experiment is shown. Numbers in dot plots represent the percentage of GzmA-positive cells for each IEL phenotype.
As positive control, macrophages were stimulated with E. coli LPS (100 ng/mL). Macrophages were incubated for 24 h at 37 C and 5% CO 2 , supernatants were collected, and the levels of IL-6 were measured by ELISA. Data are presented as mean ± SEM from two independent experiments performed by triplicates. Statistical analyses were performed by one-way ANOVA test with Bonferroni's post-test. *p < 0. 05; ***p < 0. 001. (C) PrestoBlue cell viability of macrophages stimulated with active GzmA (300 nM), GzmA inactivated with serpinb6b, 100 ng/mL E. coli LPS, or 100 ng/mL E. coli LPS with 5 mM ATP for 24 h. (D) Western blot of MC-38 cells incubated with mouse IL-6 (50 ng/mL) (as positive control), supernatant from recombinant mouse GzmA-stimulated macrophage (300 nM), or supernatant from LPS-stimulated macrophage (100 ng/mL).
Supernatant from unstimulated macrophage, supernatant from recombinant mouse GzmA-stimulated macrophage treated with mAb IL-6 or isotype (BioXCell), and supernatant from mouse IL-6 treated with mAb IL-6 or isotype (BioXCell) were added as control. The cells were stimulated with the indicated stimuli for 15 min, after which cells were lysed and western blotted using pSTAT3 antibody (Tyr705). IL-6-dependent changes in STAT3 phosphorylation were evaluated by quantifying band intensities on pSTAT3 blots and comparing to b-actin band intensities as a loading control, with the aid of ImageJ software (lower graph). GzmA belongs to a family of serine proteases traditionally recognized as anti-tumor and anti-infective agents because of the ability to trigger death on target cells in vitro. Challenging this dogma, we now show that GzmA plays a critical role in promoting gut inflammation and thereby tumor development.
Strengthening this conclusion, we show that microbiota composition, inflammatory cytokine levels, and intestinal permeability at steady state are identical in WT and in GzmA KO mice. Moreover, the extracellular GzmA inhibitor, serpinb6b, was effective when inoculated at 5 weeks, indicating that the loss of intestinal barrier during DSS/AOM treatment can be dissociated from the effect of GzmA on CRC development. GzmA is the first member of the Gzms family shown to promote gut inflammation and CRC development. GzmM has been reported to protect against colitis and CRC in the DSS/AOM model (Souza-Fonseca-Guimaraes et al. , 2016), suggesting that therapeutic approaches would need to be specific for GzmA. We did not find significant expression of GZMM in transcriptomics analysis of two cohorts of human CRC samples (data not shown).