dataesr/scientific-paragraphs-categorization huggingface dataset:
SANS has also been applied to investigating the size of lipid domains by contrast matching  (Pencer et al. , 2005; Heberle et al. , 2013)  and proteins inserted into the membrane  (Spinozzi et al. , 2022). In this work, we have presented the new SASBIN model to analyse SAXS and SANS curves from large unilamellar vesicles containing SLD inhomogeneities. These may represent pores and lipid domains distributed in the lipid bilayer, or proteins anchored or immersed in the membrane. Through development of the SASBIN model, we have shown it is possible to recognize the presence of inhomogeneities in the SAS curves, and their dimensions and spatial distribution.
Funding information Leandro Barbosa thanks the Fundaca ˜o de Amparo a Pesquisa do Estado de Sa ˜o Paulo (FAPESP, grant No. 2017/26131-5) for financial support. This work has been partially funded from the European Union's Horizon 2020 research and innovation programme (Marie Skłodowska-Curie grant agreement No. 823780) and by the European Union -Next Generation EU (project code ECS00000041, project title 'Innovation, digitalization and sustainability for the diffused economy in Central Italy -VITALITY'). Rosangela Itri and Leandro Barbosa are recipients of CNPq research fellowships.
1c ). Furthermore, we detected a substantial amount of HNRNPC binding to poly(U) elements in 3′ UTRs across our own as well as previously published HNRNPC CLIP-seq datasets  25  (Extended Data Fig. 1h ). Finally, if the poly(U) motifs in the 3′ UTRs of translationally repressed mRNAs are bound by HNRNPC, mRNAs that are bound by HNRNPC in their 3′ UTRs should also be translationally repressed in highly metastatic cells. To test this, we performed a gene-set enrichment analysis, using the set of HNRNPC-bound 3′ UTRs to assess their patterns of enrichment and depletion across the TE values from both breast cancer cell lines and PDXs. As shown in Fig. 1d ,e (and Extended Data Fig. 1i ,j), we observed a consistent enrichment of this HNRNPC regulon among the genes with lower TE in the highly metastatic cells.
Translational reprogramming accompanies breast cancer metastasis To capture changes in the translational landscape that are associated with breast cancer metastasis, we performed ribosome profiling (Ribo-seq) on a commonly used triple receptor negative model of breast cancer metastasis, MDA-MB-231 breast cancer cells and their lung metastatic derivative cell line, MDA-LM2 (ref. 15). We predominantly recovered 33-34-nucleotide-long ribosome-protected mRNA footprints, aligning in frame with annotated coding sequences (CDSs)  16  , confirming the high quality of our dataset (Extended Data Fig. 1a, b ). We then sought to measure relative changes in translational activity genome-wide by calculating translational efficiency ratios (TERs) between MDA-LM2 and parental MDA-MB-231 cells. To perform reliable differential analysis of Ribo-seq data and systematically account for possible confounders, we developed an analytical framework for comparison of translation efficiencies (TEs, representing the ratio between ribosome-protected mRNA footprint (RPF) and mRNA abundance sequencing read counts), aiming for as few a priori assumptions as possible.
APAlog functions in three modes: (1) identifying transcripts with the highest overall variability in poly(A) site usage across conditions, (2) comparing all non-canonical poly(A) sites with one canonical (reference) poly(A) site per transcript and (3) comparing all pairs of poly(A) sites per transcript (for details, see Methods). Using APAlog, we found that HNRNPC target mRNAs undergo 3′ UTR lengthening (that is, proximal-to-distal poly(A) site switch) in MDA-LM2 (Fig. 2d  and Extended Data Fig. 2e ) and HNRNPC KD (Fig. 2e  and Extended Data Fig. 2f, g ) cells. To confirm our observations in an exogenous setting, we designed a massively parallel reporter assay that simultaneously monitors reporter mRNA poly(A) site selection and translation. We used a bidirectional promoter vector, driving mCherry and blue fluorescent protein (BFP) expression, and containing two APA sites downstream of BFP.
Furthermore, gene-set enrichment analysis of this data revealed ) https://doi. org/10. 1038/s41556-023-01141-9 P = 0. 2 3 mm 3 mm H N R N P C -O E C o n t r o l s g H N R N P C s g C o n t r o l s g H N R N P C s g C o n t r o l that HNRNPC KD caused the downregulation of proteins interacting with SH3 (Src homology 3) domain proteins and actin filaments, among other Gene Ontology terms (Extended Data Fig. 5c ). To identify genes that are part of this HNRNPC regulon and act downstream of this pathway to influence metastatic progression, we systematically integrated the datasets comparing poorly and highly metastatic cells, as well as HNRNPC KD and control cells. We specifically searched for mRNAs that (1) were translationally repressed and (2) demonstrated proximal-to-distal poly(A) site switching in MDA-LM2 and HNRNPC-deficient cells (16 and 30 transcripts, respectively).
The HNRNPC-PABPC4 regulatory axis is linked with clinical outcomes To confirm that our findings in xenograft models are generalizable to human disease, we performed survival analysis on publicly available datasets from breast cancer patients. We found that lower HNRNPC expression in breast cancer tumours was significantly associated with lower overall, disease-free and distant metastasis-free survival, both in individual cohorts and in meta-analyses (Fig. 6a-c  and Extended Data Fig. 6a-c ). We detected that HNRNPC expression was negatively associated with tumour stage (Fig. 6d ) and presence of metastasis (Fig. 6e ), but not with tumour subtype (Extended Data Fig. 6d ). Notably, HNRNPC expression remained a significant covariate in a Cox proportional hazards model even after controlling for other known prognostic metrics, such as tumour stage or received treatment (Extended Data Fig.
6e ). As we found HNRNPC to act upstream of a metastasis-suppressive translational programme, we identified a set of HNRNPC mRNA targets, translationally repressed and undergoing proximal-to-distal poly(A) site switching in MDA-LM2 cells to define a translational HNRNPC target signature. We observed that, in proteomic datasets from patients with breast cancer (CPTAC), lower protein levels of the HNRNPC signature, as an aggregate, were significantly associated with lower overall and progression-free survival (Extended Data Fig. 6f, g ). In line with PABPC4 acting together with HNRNPC, lower PABPC4 expression was associated with worse prognostic metrics and disease progression in breast cancer patient cohorts (Fig. 6f ,g and Extended Data Fig. 6h ). Furthermore, in agreement with PDLIM5 being a functional effector downstream of the HNRNPC-PABPC4 axis, PDLIM5 expression was also associated with survival of patients with breast cancer (Fig.
After transduction with single guide RNA lentivirus, MDA-MB-231, MDA-LM2 and HCC1806-LM2 CRISPRi cells were selected with 1. 5 µg ml -1 puromycin or 20 µg ml -1 blasticidin (Gibco). KD of target genes was assessed by RT-qPCR as described below. We consistently observed two-to five-fold decrease in target mRNA expression, with the exception of PABPN1. PABPN1 is labelled as common essential gene in DepMap Portal database, and we observed only modest PABPN1 KD efficiency in MDA-MB-231 cells, accompanied by severe impact on cell proliferation. The EF1α promoter-driven lentiviral reporter has been described  42. PDLIM5, HNRNPC or mCherry CDSs were synthesized (Integrated DNA Technologies (IDT)) and cloned by restriction digestion (PacI) and Gibson assembly. The constructs were delivered to MDA-MB-231 or MDA-LM2 cells by lentiviral transduction and puromycin selection.
To process the reads, the Ribo-seq reads were first trimmed using cutadapt (v2. 3) to remove the linker sequence AGATCGGAAGAGCAC. The fastx_barcode_splitter script from the Fastx toolkit (v0. 0. 13) was then used to split the samples based on their barcodes. Since the reads contain UMIs, they were collapsed to retain only unique reads. The UMIs were then removed from the beginning and end of each read (two and five nucleotides, respectively) and appended to the name of each read. Bowtie2 (v2. 3. 5) was then used to remove reads that map to ribosomal RNAs and tRNAs, and the remainder of reads were then aligned to mRNAs (we used the isoform with the longest CDS for each gene as the representative). Subsequent to alignment, umitools (v0. 3. 3) was used to deduplicate reads.
RNA-seq and 3′-end RNA-seq RNA-seq libraries (used for calculating TEs) were prepared using SMARTer Stranded Total RNA-Seq Kit v2 Pico Input Mammalian (Takara), with 50 ng total RNA as input. The 3′-end RNA-seq libraries (used for determining poly(A) site usage) were prepared using Quant-Seq 3′ mRNA-Seq Library Prep Kit REV (Lexogen), with 500 ng total RNA as input. Libraries were sequenced as SE50 runs on Illumina HiSeq4000 instrument at UCSF Center for Advanced Technologies. To compare changes in 3′ UTR usage and poly(A) site selection, we first annotated unique 3′ ends of transcripts using Gencode annotations (v33). Salmon (v0. 14. 1) was then used to count the number of reads that match each of the annotated ends. The normalized abundances were then tabulated, and APAlog (see below) was used to perform pairwise comparisons between proximal and distal poly(A) sites between conditions.
To assess whether HNRNPC binding was associated with the observed changes in logAPAR values, proximal sites within 500 nt of annotated HNRNPC peaks (based on CLIP-seq datasets) were annotated. The behaviour of these HNRNPC-associated proximal sites was then compared with the background using Wilcoxon rank sum test. Alternatively, logAPAR values were binned into equally populated bins, and the enrichment/depletion patterns of HNRNPC-associated proximal sites were assessed as previously described  23.
The libraries were then sequenced as SE50 runs on Illumina HiSeq4000 instrument at UCSF Center for Advanced Technologies. The CTK package (v1. 1. 13, CLIP toolkit  46  ) was used to annotate peaks from CLIP-seq data. Reads were first collapsed using their UMIs. UMI-tools package was then used to extract the UMI followed by quality trimming (-q 15) and linker removal using cutadapt. BWA (v0. 7. 17) was then used to align reads to the genome (hg38). CTK scripts were then used to remove PCR duplicates, parse alignments and call peaks using a valley-seeking algorithm (with multi-testing correction). The boundaries of the resulting peaks were combined across multiple independent CLIP experiments, and their union with a previously published HNRNPC iCLIP (E-MTAB-1371) was used to define a comprehensive HNRNPC binding across the transcriptome.
7 m/z). Singly and unassigned charge states were rejected. Dynamic exclusion was enabled with a repeat count of 1, an exclusion duration of 20 s and an exclusion mass width of ±10 p. p. m. Data were collected using the MS3 method  47  for obtaining TMT tag ratios with MS3 scans collected in the orbitrap at a resolution of 60,000, HCD collision energy of 65% and a scan range of 100-500. Protein identification and quantification were done with Integrated Proteomics Pipeline (IP2, Integrated Proteomics Applications) using ProLuCID/Sequest, DTASelect2 and Census  48, 49. Tandem mass spectra were extracted into ms1, ms2 and ms3 files from raw files using RawExtractor  50  and were searched against the UniProt human protein database plus sequences of common contaminants, concatenated to a decoy database in which the sequence for each entry in the original database was reversed  51.
Search space included all fully tryptic peptide candidates with no missed cleavage restrictions. Carbamidomethylation (+57. 02146) of cysteine was considered a static modification; TMT tag masses, as given in the TMT kit product sheet, were also considered static modifications. We required one peptide per protein and both tryptic termini for each peptide identification. The ProLuCID search results were assembled and filtered using the DTASelect program with a peptide false discovery rate (FDR) of 0. 001 for single peptides and a peptide FDR of 0. 005 for additional peptides for the same protein. Under such filtering conditions, the estimated FDR was between zero and 0. 06 for the datasets used. Quantitative analysis on MS3-based Mul-tiNotch TMT data was analysed with Census 2 in IP2 platform  47, 52, 53.
Briefly, 6 µl of each peptide sample was resolved at 250 nl min -1 flow rate on an Easy-Spray 50 cm × 75 µm RSLC C18 column (Thermo Fisher), using a 123 min gradient of 3% to 35% of buffer B (0. 1% formic acid in acetonitrile) against buffer A (0. 1% formic acid in water), followed by online infusion into the mass spectrometer by electrospray (1. 95 kV, 255C). The mass spectrometer was operated in data-dependent positive mode. A TOP15 method in which each MS scan is followed by 15 MS/MS scans was applied. The scans were acquired at 375-1,500 m/z range, with a resolution of 70,000 (MS) and 17,500 (MS/ MS). A 30 s dynamic exclusion was applied. MaxQuant (v1. 6. 3. 3) was used for all MS search and protein quantifications. All downstream MS data analysis was performed using Perseus (v1. 6. 2.
Metastatic colonization assay Mice were housed in accordance with UCSF IACUC protocol (approval number AN179718) in humidity-and temperature-controlled rooms on a 12 h light-dark cycle with free access to food and water. Seven-to 12-week-old age-matched female NSG mice ( Jackson Labs, 005557) were used for lung colonization assays. For this assay, cancer cells constitutively expressing luciferase were suspended in 100 µl PBS and then injected via tail vein (2. 5 × 10 4 for MDA-LM2, 5 × 10 4 for MDA-MB-231 and 1 × 10 5 for HCC1806-LM2). Each cohort contained four to five mice, which in NSG background is enough to observe a more than two-fold difference with 90% confidence. Mice were randomly assigned into cohorts. Cancer cell growth was monitored in vivo at the indicated times by retro-orbital injection of 100 µl of 15 mg ml -1 luciferin (PerkinElmer) dissolved in 1× PBS, and then measuring the resulting bioluminescence with an IVIS instrument and Living Image software (PerkinElmer).
Moreover, the ratio of two NB variables does not follow any known statistical distribution; therefore, inference on TER using parametric significance tests remains a challenge. We thus sought to devise a new analytical framework for reliable comparison of TEs across conditions with fewer a priori assumptions. The resulting method, which we have named Ribolog, relies on logistic regression to model individual Ribo-seq and RNA-seq reads to estimate logTER (that is, log fold change in TE) and its associated P value across the coding transcriptome. Before entering the Ribolog pipeline, RNA and RPF fastq files are pre-processed, as described above. Sorted and indexed bam files are imported into Ribolog, and mapped reads are assigned to specific codons using functions borrowed from the R package riboWaltz  61.
Then, we calculate the CELP bias coefficient and the bias-corrected read count as: The reasons for using loess-smoothed counts and not raw counts in the above calculations are three-fold: (1) In our experience with multiple ribosome profiling datasets, we have observed that stalling peaks often appear in the same approximate position, but not necessarily the same exact codon, even among replicates of a single biological sample. (2) Some of the factors that impede translation, for example, RBP binding or RNA secondary structures, affect several adjacent codons, not a single codon. (3) Calculation of P-site offset and assignment of RPF reads to specific codons carries a degree of uncertainty, because the distance of read ends from start or stop codon, which is used to estimate P-site offset, is always a distribution, not a single value, even for reads of the same length.
On the other hand, if CELP is primarily used to debias RPF counts to allow an unbiased TER test, all samples in the dataset can be pooled together in the calculation of bias coefficients. TER test. Both RNA and RPF libraries are mapped to the same reference transcriptome as described in the previous section. RNA read counts per transcript are calculated directly from the bam files. RPF read counts are either obtained in the same way or run through CELP debiasing first to smooth out local non-uniformities (described above in detail). RNA and RPF 'transcript × sample' count matrices are normalized separately for library size variation using the median-of-ratios method  62. We model TE as the odds of retrieving two different sequencing read types from a sample: RPF versus RNA. In this scenario, we hypothetically pool all the reads from an experiment, and then extract a read from this pool.
The expected TER of a transcript between two samples differing in one or multiple attributes can be estimated by substituting the obtained regression coefficients in the equation below: TER = TE 2 TE 1 = exp {∑ i β i (X i,2 -X i,1 )} For detailed instructions to install the package, prepare the input data, run the tests, and interpret and plot the results, visit https:// github. com/goodarzilab/Ribolog. Additional modules for quality control, empirical null significance testing to reduce false positives, meta-analysis of ribosome profiling data and so on are also available from the GitHub page. We used the data simulated by the authors of Xtail to benchmark Ribolog against four other commonly used tools (Xtail, Riborex, RiboDiff and Anota2seq). The results are available at https://github. com/goodarzilab/Ribolog/blob/master/benchmarks/ ribolog_benchmarks.
pdf. APAlog. The RNA reads used to compare poly(A) site usage could originate from a regular RNA-seq or a specialized 3′ UTR sequencing protocol. In either case, normalized counts of reads mapped to each poly(A) site are used by APAlog to assess the extent and pattern of differential poly(A) site usage via multinomial logistic regression: APAlog automatically sets the poly(A) site of each transcript that comes first alphabetically to reference. The user can specify which poly(A) site to serve as reference by adjusting the poly(A) site names in the count matrix. APAlog can be run in three modes: (1) Overall transcript-wise test: a deviance test is performed between the fitted model with covariates and the null (intercept-only) model. This test identifies transcripts that show differential poly(A) site selection among samples but does not specify which poly(A) sites or covariates contribute to the difference.
This mode facilitates the quick scanning of a large multi-group dataset to flag putative targets of regulation. Moreover, by performing exactly one test per transcript, it avoids complications of multiple testing correction among transcripts with unequal number of poly(A) sites. (2) Alternatives versus reference test: One poly(A) site per transcript is marked as the reference site, and all others (one or more) are tested against it. This mode is suitable for specific applications such as testing 3′ UTR length variation when one poly(A) site, in this case the most proximal one, can be set to reference and all others compared with it. (3) Pairwise test: this test compares all pairs of poly(A) sites per transcript and provides the highest-resolution view of poly(A) site selection regulation. It is also the best choice if a reference or canonical poly(A) site cannot be logically assigned.
Statistics and reproducibility For in vivo experiments, mice were distributed into cohorts with five mice per cohort, which in NSG background is enough to observe a more than two-fold difference with 90% confidence. For other experiments, no statistical methods were used to calculate sample size. No data were excluded from the analyses. Cell migration/invasion assays were performed in four biological replicates. Co-IP-MS, TMT-MS, RT-qPCR and western blot experiments were performed in biological triplicates. Sequencing-based experiments (Ribo-seq, RNA-seq, CLIP-seq, PAPERCLIP and massively parallel reporter assays (MPRA)) were performed in biological duplicates. Mice for in vivo experiments were randomly assigned into cohorts. For other experiments, no randomization was performed. For cell migration/invasion assays, the person counting the colonies was blinded for the experimental conditions.
Acknowledgements We acknowledge the  UCSF Center for Advanced Technology (CAT)  for high-throughput sequencing and other genomic analyses. We thank  S. F. Tavazoie  for the gift of the  HCC1806  and  HCC1806-LM2  cell lines. We thank the  Preclinical Therapeutics core as well as the Laboratory Animal Resource Center (LARC)  at  UCSF. We acknowledge support from our colleagues at the  Helen Diller Family Comprehensive Cancer Center  and the  Breast Oncology Program. Funding: This work was supported by grants from the  NIH  ( R00CA194077  and  R01CA240984 ) and  ACS  ( 130920-RSG-17-114-01-RMC ) to H. G. This research was also supported by funding from the  UCSF Helen Diller Family Comprehensive Cancer Center Breast Oncology Program  (the content is solely the responsibility of the authors).
Data availability All sequencing data have been deposited in the GEO database under accession GSE186647. Proteomics data have been deposited in the PRIDE database under accession PXD029560. A previously published HNRNPC iCLIP dataset (E-MTAB-1371) was used in this study. The human breast cancer data were derived from the TCGA (at Genomic Data Commons, https://gdc. cancer. gov). The METABRIC dataset was obtained from cBioPortal (https://www. cbioportal. org). The CPTAC breast cancer dataset was obtained from Proteomics Data Commons (https://proteomic. datacommons. cancer. gov/pdc/). All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.
6%) during defensive phases compared to offensive phases. Some studies on women's handball looking at the quantification of high intensity events using IMUs have been reported. Two of those studies  [7, 8]  collected data on 20 players belonging to the Norwegian women's national team during international matches. The aim was to quantify high-intensity match events and handball activity profiles using IMUs. Findings showed that high-intensity activities are positionspecific and that their intensity depends on the role of the player in the team (offensive specialist, defensive specialist, or player playing in both situations). Furthermore, the activity does not seem to be sustained throughout the match, and intensity seems to be highest in the first ten minutes. In those studies, authors used the PlayerLoad TM metric.
Data sample Data was collected on a Spanish first division handball club (highest level of competition in Spain) and covered 31 matches over two seasons  (2019-2020 and 2020-2021). This club finished top 5 nationally over these two years. In this sample, all the games were played at home and we equipped all the players with a sensor including an IMU and linked to a LPS based in the club arena. Over those games, 27 different male handball players appeared in the team (average SD for height: 1. 90 ± 0. 06 m; weight: 91. 9 ± 12. 5 kg and age: 26. 3 ± 3. 9 years old). For each game, the group was composed of 14 to 16 players from the pool of 27. The players performed on average five handball training sessions and two to four strength and conditioning sessions per week during the season. Each session last from 1.