dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of regulatory miRNA clusters underlying breast cancer subtypes We applied ClustMMRA to identify polycistronic miRNAs underlying breast cancer molecular subtypes. For this study, two independent datasets were used, a first paired miRNA/mRNA expression dataset from a in-house cohort of 129 breast carcinoma tumour samples (which we refer to as Curie dataset  35, 36  and a second dataset from The Cancer Genome Atlas project composed of 397 samples  37. In both datasets, individual samples were assigned to four subtypes (luminal A, luminal B, HER2+ and triple negative) based on the immunohistochemical staining of estrogen (ER), progesterone (PR) and HER-2 (ERBB2) receptors.
ClustMMRA application to Curie and TCGA datasets Expression data required for running ClustMMRA were pre-processed as described in Methods and the signatures for breast cancer subtypes were defined using the approach proposed in  38  (see Methods). We applied the ClustMMRA pipeline on Curie and TCGA datasets separately. In the first step, genomically co-clustered miRNAs having a subtype-specific expression were identified. In this step, 28 and 47 out of 131 analyzed clustered miRNAs were selected for Curie and TCGA datasets, respectively (see Supplementary Table  S1 ). Of these, 18 clusters were in common between the two datasets (p-value<7e-04), revealing a significantly concordant expression pattern of co-clustered miRNAs. Among these co-clustered and coexpressed miRNAs, some are differentially expressed in multiple subtypes (18 and 37 clusters for Curie and TCGA respectively), with 15 out of 18 and 21 out of 37 differentially expressed in basal-like and luminal A with opposite sign.
In step (ii), 10 out of 28 (Curie) and 16 out of 47 (TCGA) subtype-specific miRNA clusters were found to have their predicted targets enriched in genes belonging to the corresponding gene signature. The output of step (ii) (see Supplementary Table  S2 ) has an intersection of 7 elements between the two datasets (p-value <1e-05). In the step (iii) of ClustMMRA, a regulon for each miRNA cluster selected in step (ii) was constructed. The regulons were tested for enrichment in gene signature. 7 out of 10 and 9 out of 16 clusters passed this last selection step in Curie and TCGA datasets, respectively. These clusters constitute the final output of ClustMMRA and are reported in Table  1. After this last step, the output in common between the two datasets contains 5 clusters (p-value <8e-06). The significant overlap between results obtained from the analysis of two independent datasets with ClustMMRA supports the reliability of this approach.
Prioritization of miRNA clusters for functional validation in cell lines Before experimental validation of the ClustMMRA output, prioritization of results was performed. We considered the 5 clusters identified both in TCGA and Curie datasets. For the regulons associated to each cluster, the nodes present in both TCGA and Curie datasets were kept, obtaining a network for each regulon with size of about 100 nodes. Then, biological processes and pathways associated to these regulons were identified through Fisher's exact enrichment test, using MSigDB 47 as reference collection of signatures for pathways and biological functions. The complete list of MSigDB pathways resulting from this analysis (FDR < 0. 05) is reported in Supplementary Table  S3. Overall, the network analysis shows a regulation of ("IGLESIAS_E2F_TARGETS_UP") and angiogenesis ("GO_VASCULATURE_DEVELOPMENT","GO_CIRCULATORY_SYSTEM_DEVEL OPMENT").
Discussion Over the last two decades there has been an explosion of research focused on miRNAs involvement in cancer initiation and progression, pointing out the potential of these small RNAs as biomarkers for diagnosis, prognosis and response to treatment. However, the majority of computational and experimental approaches for the identification of master miRNA regulators involved in cancer onset and subtyping are typically designed to detect the regulatory effect of a single miRNA. This can be a limitation in identifying regulation by multiple miRNA species acting cooperatively on cellular pathways and pathological changes. The computational pipeline here described, ClustMMRA, was specifically designed to search for genomically clustered miRNAs potentially driving cancer subtyping. ClustMMRA provides a computational framework to systematically investigate polycistronic miRNA transcripts involved in cancer subtyping or possibly in other biological contexts.
Datasets preprocessing Breast cancer (BRCA) RNA-seq and miRNA-seq Level 3 expression profiles were downloaded from The Cancer Genome Atlas (TCGA) in January 2016. Only those primary tumors profiled for both mRNA and miRNA expression were included in the analysis, obtaining a total of 397 samples. Two expression matrices (one for mRNAs and the second for miRNAs) were normalized obtaining the paired mRNA/miRNA expression dataset here referred to as TCGA. The Curie dataset was generated with microarray technologies (Agilent miRNA microarray kit V3 for miRNAs and Affymetrix U133plus2 for mRNA) and pre-processed following the procedure described in  54.
Definition of a gene signature for each breast cancer subtype The ClustMMRA pipeline requires as input a gene signature for each disease subtype. Available signatures for breast cancer subtypes, such as the PAM50  55  , were not applicable here due to their limited size in terms of number of genes. We thus defined the signatures for our breast cancer study using the approach proposed in  38. The Curie dataset was used for signature construction, while the TCGA dataset was employed for signature validation. Differential gene expression for each subtype vs. all the other samples was computed by Student's t-test and log fold change cutoff (t-test adjusted p-value < 0. 05 and absolute(log fold change) > 0. 5). Moreover, to increase the predictive power of the constructed signatures, those genes associated to more than one class according to the previous criteria, or having a difference between the first and second highest absolute(log fold changes) lower than 0.
Cell culture and miRNA modulation For in vitro studies, we used two human BC epithelial cell lines: T47D and MDA-MB-231 cells (ICLC-Biologic Bank and Cell Factory, Italy). These cell lines were chosen as they represent a model of luminal A and TNBC cell lines, respectively  48. Following the manufacturer's recommendation, we maintained the cell lines within a humidified atmosphere containing 5% CO2 at 37 °C in DMEM (for T47D cell line) or advanced DMEM (for MDA-MB-231 cell line) cell culture medium (Gibco, Life Technologies), with 10% fetal bovine serum (FBS), 1% penicillin-streptomycin, 2mM glutamine (all from Lonza, Euroclone). Dulbecco Phosphate-Buffered Saline (D-PBS), trypsin, and all the media additives were obtained by Lonza (Euroclone). The sense (S) oligonucleotide sequence of each miRNA of the cluster has been designed following the sequences indicated in miRbase database  51.
2 Comparison of ClustMMRA with the pipeline for the identification of single master miRNA regulators (MMRA) We compared the results of ClustMMRA in the breast cancer study with those obtained by applying to the same dataset the MMRA pipeline for the identification of single master miRNA regulators. The goal is to investigate if the regulatory effect of a cluster can be detected by studying the effect of individual miRNAs belonging to the same cluster. We applied MMRA to the Curie dataset, using in each step the same thresholds employed for ClustMMRA. If at least two miRNAs of a given cluster are included in the output of MMRA, we consider this cluster as detected in the single-miRNA pipeline. Interestingly, 4 out of 7 clusters detected by ClustMMRA (miR-199a/214, miR-493/136, miR-512/373 and miR-450b/424) were not detected by MMRA.
The role of genetic variability in dementia with Lewy bodies (DLB) is now indisputable; however, data regarding copy number variation (CNV) in this disease has been lacking. Here, we used whole-genome genotyping of 1454 DLB cases and 1525 controls to assess copy number variability. We used 2 algorithms to confidently detect CNVs, performed a case-control association analysis, screened for candidate CNVs previously associated with DLB-related diseases, and performed a candidate gene approach to fully explore the data. We identified 5 CNV regions with a significant genome-wide association to DLB; 2 of these were only present in cases and absent from publicly available databases: one of the regions overlapped LAPTM4B, a known lysosomal protein, whereas the other overlapped the NME1 locus and SPAG9.
Sample selection A total of 1454 patients diagnosed with DLB and of European ancestry were selected for this study. Diagnosis of DLB was made according to clinical or pathological criteria  (McKeith et al. , 2005) , Briefly, these included 298 clinically diagnosed and 1156 neuropathologically diagnosed cases. Detailed sample and processing information has been described previously  (Guerreiro et al. , 2018). Data from 1525 control samples were obtained from The Genetic Architecture of Smoking and Smoking Cessation study (phs000404. vl. pl) publicly available at the database of genotypes and phenotypes (https:// www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi?study_id=phs000404. vl. pl). Supplementary Fig. 1  shows an overview of the study design, different quality control (QC) steps, and analyses performed.
Genotyping, quality control, and CNV calling Seven hundred fifty-four DLB samples were genotyped on HumanOmni2. 5Exome-8 vl. O. B Illumina arrays, and 700 DLB samples were genotyped using Infinium OmniExpress-24 vl. 2. Al Illumina arrays (Illumina, Inc, CA, USA). Control samples were genotyped on HumanOmni2. 5-4 vl. D arrays (Illumina, Inc, CA, USA). Intensity files were analyzed using GenomeStudio v2011. 1 software (Illumina, Inc, CA, USA) along with the respective manufacturer's cluster files. QC procedures were performed in GenomeStudio (GS) before CNV analysis as described by Jarick et al. (Jarick et al. , 2014). In short, samples with call rates lower than 0. 97 were filtered out. SNP statistics were recalculated following visual inspection of B allele frequency (BAF) and log R ratio (LRR) plots.
We used the database of genomic variants (DGV) (http://dgv. tcag. ca/dgv/app/home, accessed November 2017) to determine the population frequency of CNVs  (MacDonald et al. , 2014). This information was complemented with the frequency from clinical samples available in DECIPHER v9. 18 (https://decipher. sanger. ac. uk/, accessed November 2017).
CNV calling and QC steps After QC steps at the GS level, a total of 2819 samples (1294 cases and 1525 controls) remained for further analyses. From the 754 DLB samples genotyped with HumanOmni2. 5 arrays, 616 (81. 7%) samples were kept and from the 2,567,845 probes in this array, 2,496,600 (97. 2%) passed QC. Six hundred seventy-eight (96. 9%) samples of the 700 samples genotyped with OmniExpress arrays passed QC and from the 713,599 probes available in this chip, 698,680 (97. 9%) probes remained. All controls from the Smoking Cessation database had good quality genotypes (call rate > 0. 97) and, consequently, no samples were excluded, and 2,390,384 (97. 8%) of the 2,443,177 probes were kept. After combining the results obtained by the 2 CNV calling algorithms (cnvPartition and PennCNV), excluding samples because of their relatedness, and performing the PennCNV QC steps on the LRR and BAF values and number of calls, a final number of 2615 individuals (1187 DLB cases and 1428 control samples) and 80,416 CNVs were analyzed.
Case-control association analysis Of the 494 CNV regions resulting from the ParseCNV analysis, only 5 passed QC checks and were statistically significant (Table  1 ). Of these, 2 of the regions were not present in our control population or in public databases: the deletion overlapping LAPTM4B (p = 6. 29 × 10 -7 ) and the one overlapping SPAG9-NME1-NME2 (p = 2. 72 × 10 -4 ) (Fig. 1 ).
Candidate CNVs approach Five CNVs previously associated with DLB-related neurodegenerative diseases were found in DLB patients (Table  1 ). Two of these were present in the control group with a higher frequency than in the patient group, and the remaining 3 are described in public databases and are detailed next. The duplication identified on chromosome 12 overlapping DDXU and OVOS2 has a frequency 10-fold higher in DGV than in the DLB cohort. The 16pl 1. 2 microduplication found in one DLB patient has a frequency of 1. 69 × 10 -4 in the DECIPHER database but does not occur in any control samples or in the DGV database. One of the CNVs that was previously significantly associated with AD locates at chr8:2,792,874-4,852,328 and overlaps CSMD1  (Swaminathan et al. , 2011). CSMD1 has frequent deletions and duplications spanning the whole gene as reported in DGV.
Candidate genes approach We investigated CNVs in genes known to be associated with diseases that are related to DLB (Supplementary Table  2 ) and identified a total of 8 CNVs (Table  1 ). These included one duplication in APP occurring in a clinically diagnosed case. This large duplication is not present in the databases or in the control cohort. Two DLB subjects were found to carry duplications spanning MAPT, and one neuropathologically diagnosed patient was found to cany a SNCA duplication (Fig. 2 ). PARK2 was found to have many copy number losses and gains in controls (n = 28) and cases (n = 13) but none were homozygous. A duplication including CHCHD10 was also identified in a neuropathologically diagnosed DLB patient. No CNVs were identified in CBA, the gene presenting the strongest association with DLB.
, 2018). Although we cannot directly link this CNV to the development of DLB in these cases, it is interesting that a lysosomal enzyme is the top hit in our association analysis, given the prominent role of the lysosome in Lewy body diseases. In fact, a strong GWAS hit for DLB is GBA, a gene involved in lysosomal lipid storage disorders. Also associated with DLB and absent from publicly available databases was a deletion overlapping theNME1 locus. NME1 is involved in purine metabolism, which has been reported to be disrupted in AD, PD, and Creutzfeldt-Jakob disease. NME1 mRNA was also found to be reduced in these diseases  (Ansoleaga etal. ,2015 (Ansoleaga etal. , ,2016;; Garcia-Esparcia et al. , 2015). Here, we identified a deletion at the 3'-end of NME1, which could be consistent with a reduced expression of the gene in DLB, although this was not tested in the present study.
, 2006; Obi et al. , 2008). Four neuropathologically diagnosed DLB cases presented a large duplication from DSPP to PDLIM5 including SNCA, 3 of these were heterozygous and 1 was homozygous  (Ikeuchi et al. , 2008). A duplication in SNCA was also described in a probable DLB patient in a study with 99 cases  (Meeus et al. , 2012). Here, we add to this body of evidence, by identifying another patient neuropathologically diagnosed with DLB carrying a SNCA duplication. In our DLB cohort, this duplication shows a similar frequency to that provided by DGV. However, the frequency reported in DGV results from 2 entries in that database. When looking in more detail at these 2 entries, they are actually duplicated entries from the same Human Genome Diversity Project (HGDP) sample from Cambodia (HGDP00721).
Authors Celia Kun-Rodrigues a , Tatiana Orme a,b , Susana Carmona a,b , Dena G. Hernandez c,d , Owen A. Ross e , John D. Eicher f , Claire Shepherd g , Laura Wales, and GMH is funded by an NHMRC senior principal research fellowship. The authors thank the South West Dementia Brain Bank (SWDBB) for providing brain tissue for this study. The SWDBB is supported by BRACE (Bristol Research into Alzheimer's and Care of the Elderly), Brains for Dementia Research, and the Medical Research Council. The authors acknowledge the Oxford Brain Bank, supported by the Medical Research Council (MRC), Brains for Dementia Research (BDR) (Alzheimer Society and Alzheimer Research UK), Autistica UK, and the N1HR Oxford Biomedical Research Center. The brain samples and/or bio samples were obtained from The Netherlands Brain Bank, Netherlands Institute for Neuroscience, Amsterdam (open access: www.
brainbank. nl). All materials have been collected from donors for or from whom a written informed consent for a brain autopsy, and the use of the material and clinical information for research purposes had been obtained by the NBB. This study was also partially funded by the Wellcome Trust, and the Canadian Consortium on Neurodegeneration and Aging of the Canadian Institutes of Health Research (PSGH). Work from YC was supported by the CERCA Program/ Generalitat de Catalunya, Barcelona, Catalonia, Spain. The Nottingham Genetics Group is supported by ARUK and The Big Lottery Fund. The effort from Columbia University was supported by the Taub Institute, the Panasci Fund, the Parkinson's Disease Foundation, and NIH grants NS060113 (LC), P50AG008702 (P. I. Scott Small), P50NS038370 (P. I. R. Burke), and UL1TR000040 (P.
Fox Foundation for Parkinson's Research, N1NDS R01# NS078086. The Mayo Clinic Jacksonville is a Morris K. Udall Parkinson's Disease Research Center of Excellence (N1NDS P50 #NS072187) and is supported by The Little Family Foundation and by the Mangurian Foundation Program for Lewy Body Dementia research and the Alzheimer Disease Research Center (P50 AG016547). The work from the Mayo Clinic Rochester is supported by the National Institute on Aging (P50 AG016574 and U01 AG006786). This work has received support from The Queen Square Brain Bank at the UCL Institute of Neurology, where TL is funded by an ARUK senior fellowship. Some of the tissue samples studied were provided by the MRC London Neurodegenerative Diseases Brain Bank and the Brains for Dementia Research project (funded by Alzheimer's Society and ARUK).
This research was supported in part by both the NIHR UCLH Biomedical Research Center and the Queen Square Dementia Biomedical Research Unit. This work was supported in part by the Intramural Research Program of the National Institute on Aging, National Institutes of Health, Department of Health and Human Services; project AG000951-12. The University of Pennsylvania case collection is funded by the Penn Alzheimer's Disease Core Center (AG10124) and the Penn Morris K. Udall Parkinson's Disease Research Center (NS053488). Tissue samples from UCSD are supported by NIH grant AG05131. The authors thank the brain bank GIE NeuroCFB, the French program "Investissements d'avenir" (ANR-10-IAIHU-06). PJT and LM are supported by the Helsinki University Central Hospital and Sigrid juselius Foundation and the Finnish Academy.
Data analysis Analyses were run using maximum intensity projections so that all the fluorescence emitted in the whole nuclei was accounted for. These microscopic images were analyzed using Image J software. 1. First of all, in order to minimize the signal caused by the autofluorescence of the tissues-either natural or induced by fixation, grey level intensities coming from the green and red tracks were decreased independently on each photo until no green or red fluorescence could be visually detected in tissues where the FBNSV is known to be absent (i. e. , in xylem and mesophyll; the FBNSV being a phloem restricted virus). This computer process was done in the exact same way for all pictures and applied equally to the whole surface of the image (Figure  6 ). Note: The aim of this step was to simply distinguish infected cells from non-infected ones with a high level of background autofluorescence.
As a consequence, only cells in which green or red fluorescence levels exceeded the level of background fluorescence were considered for further analysis. As we were mainly interested in the cells in which our ssDNA virus can replicate, we focused our analyses on phloem nucleated cells; nuclei stained using DAPI. 2. On the images, we manually delineated the DAPI stained area (corresponding to the nucleus in each cell) using a cursor and quantified intensity of grey level values for each pixel using the ImageJ software. The intensity of grey level was also determined for both green and red fluorescence. 3. We then calculated the average pixel fluorescence intensity value for each nucleus and each fluorochrome. We thus obtained an average intensity value (in arbitrary fluorescence unit) for both green and red fluorescence for each selected individual cell within an infected petiole (the FBNSV replicate and accumulate in the nucleus of infected cells).
1a ) and relaxation also was significantly lower than in D6-500 spheroids (Fig. 1c ). Accordingly, mathematical modeling and simulation indicated that the stored solid stress accumulated during spheroid growth was 1. 67-fold higher in D6-500 spheroids (0. 276 ± 0. 037) than in D2-5000 spheroids (0. 166 ± 0. 037) (Fig. 2 ). These data show that growth-induced solid stress is lower in D2-5000 spheroids and that the spheroid production conditions have a very significant impact on this physical property. Growth-induced solid stress is associated with difference in spherical shape acquisition depending on the spheroid production conditions. We next investigated the relationship between accumulation of solid stress and spheroid multicellular organization. We used light sheet fluorescence microscopy coupled to 3D image analysis to evaluate the volume and flatness of D6-500 and D2-5000 spheroids.
The major technical challenge was to immobilize the spheroids for probing the surface with the AFM cantilever. To this aim, we designed and produced 200 µm-high polydimethylsiloxane micro-well arrays with an internal diameter of 450 µm, which corresponds to the spheroid diameter (see Methods and Supplementary Fig. S4 ). We positioned D6-500 and D2-5000 spheroids in the micro-wells and then analyzed their surface stiffness by measuring the elastic modulus. To take into account the high spheroid size (in relation to the surface analyzed by AFM) and the possible stiffness heterogeneity due to variations between cells and cell-cell junctions, we analyzed different 10 µm 2 zones in each spheroid to determine an average Young's modulus (see Methods section and Supplementary Fig. S4 ). The Young's modulus values varied between 2 kPa and 10 kPa, in agreement with data obtained in soft tissues  20.
Analysis of the response to irinotecan treatment. After 6 days (D6-500) or 2 days (D2-5000) of culture, 10 µl of culture medium containing 10 times the required irinotecan (SIGMA #I1406) concentration was added to each well that contained 90 µl of culture medium (n = 6 spheroids/concentration). At the time of irinotecan addition and after 24 h and 48 h of incubation, bright-field images of each live spheroid were acquired with a 5x objective and an inverted microscope (AxioObserver, Zeiss GmbH). Segmentation and shape descriptors were quantified with the ImageJ software  26. Growth rates at 24 h and 48 h were calculated for each spheroid, relative to the spheroid area at the time of irinotecan addition. For each experiment, the average growth rate at each irinotecan concentration was normalized to the average growth rate of untreated spheroids.
The deflection sensitivity of the MLCT cantilever (Bruker, Germany) was obtained by calculating the force-distance curve on the glass bottom of the Petri dish 30 min after placing the cantilever in the liquid medium to allow the cantilever to reach the thermal equilibrium. The thermal tune method available for the AFM system was used to calibrate the spring constant of the cantilever far from the surface. The calibrated spring constants were 0. 13-0. 16 N/m. The "force mapping" mode was used to calculate the force-distance curves over 100 µm 2 zones on top of each spheroid. The maximum applied force was kept at 1 nN during force mapping and for the initial approach. The JPK Data analysis software was used to extract the apparent Young's modulus form each force curve. Each approach curve was first smoothed using a 3 pixels Gaussian filter, and then force vs.