dataesr/scientific-paragraphs-categorization huggingface dataset:
Notably, the pancreatic adenocarcinoma (PAAD) dataset contains DNA alterations or methylation, and messenger RNA (mRNA), microRNA (miRNA), long noncoding RNA (lncRNA), and protein expression profiles of 184 patients (including 168 with available mRNA data) that can be used to investigate the involvement of genes of interest  [9]. Mucins are large O-glycoproteins that are classified into two subgroups: (i) secreted mucins (MUC2, -5AC, -5B, -6, -7, -8, -9/OVGP1 and -  19) , which are the main components of mucus covering the different epithelia  [10] [11] [12] [13] , and (ii) membrane-bound or transmembrane mucins (MUC1, -3A/B, -4, -12, -13, -15, -16, -17, -20, -21, and -22), which are bound to cell surface and are involved in interactions with the microenvironment and regulate cell signaling  [11, 14].
In the present manuscript, we used web-tools such as GEPIA, cBioPortal, SurvExpress, and PROGgeneV2 to investigate the expression pattern of every mucin gene in the pancreatic cancer PAAD dataset available from the TCGA consortium. We analyzed the impact of their expression on patient survival and the correlation of mucin gene expression, and we performed a hierarchical clustering analysis of PAAD cohort. We identified two major subgroups: one with a membrane-bound mucin signature (MUC1, -4, -16, -17, -20, and -21; signature #2) and the other one with an atypical mucin signature (MUC15, MUC14/EMCN, and MUC18/MCAM, signature #1). Signature #2 is associated with shorter overall survival and is proposed as a poor prognosis signature.
Mucin Expression Patterns in Pancreatic Adenocarcinoma In order to study mucin gene expression, we took advantage of publicly available datasets. We analyzed the expression of every mucin gene in pancreatic adenocarcinoma samples compared to normal pancreas tissues using GEPIA to compile Genome Tissue Expression (GTEX) (normal) and TCGA (tumor) datasets. We generated boxplots for genes encoding membrane-bound mucins (MUC1, MUC3A, MUC4, MUC12, MUC13, MUC15, MUC16, MUC17, MUC20, MUC21 and MUC22) (Figure  1A ), genes encoding secreted mucins (MUC2, MUC5AC, MUC5B, MUC6, MUC7, MUC9/OVGP1, and MUC19) (Figure  1B ), and the two atypical mucin genes MUC14/EMCN and MUC18/MCAM (Figure  1C ). We observed a statistically significant increased expression in tumor samples for a majority of the genes encoding membrane-bound mucins (MUC1, -3A, 4, -12, -13, -16, -17, and -20) (p < 0.
01) (Figure  1A ). Only MUC15, MUC21, and MUC22 mRNA did not harbor any significant expression variation. Two secreted mucin mRNA (MUC5AC and MUC5B) were also increased in tumor samples (p < 0. 01), whereas MUC6 was decreased in tumor samples compared to the normal pancreas (Figure  1B ). The atypical mucins mRNA (MUC14/EMCN and MUC18/MCAM) were also increased in tumor samples (p < 0. 01) (Figure  1C ). Moreover, our analysis of the GSE28735 dataset, which contains 45 tumor samples and 45 paired adjacent nontumoral samples, showed a similar pattern with a significant increase in membrane-bound MUC1, MUC3, MUC4, MUC13, MUC16, MUC17, MUC20, and MUC21 mucin mRNA (Figure  S1A ) and the MUC5B secreted mucin mRNA (Figure  S1B ). We observed a decrease in the expression of genes encoding membrane-bound MUC15 (Figure  S1A ) and secreted MUC7 and MUC19 in tumor samples (Figure  S1B ) (p < 0.
36, p = 0. 03346). We then conducted a similar analysis using PROGgeneV2 that splits the PAAD cohort into two cohorts on the basis of the median of expression. We confirmed a similar association between high expression of MUC1, MUC4, MUC16, MUC17, MUC20, and MUC21 and shorter patient survival (Figure  S2 ). In addition, high expression of MUC2 and MUC5B was also associated with a worse prognosis in this analysis. On the contrary, MUC9 high expression was correlated with a better outcome of PAAD patients. Finally, the analysis of GSE57495 containing 63 patients showed a trend toward an association of MUC1, MUC4, MUC16, MUC17, and MUC20 with a worse outcome that was close to statistical significance (Figure  S2 ).
Mucin Genomic Alterations in Pancreatic Adenocarcinoma We extracted genomic alterations and RNA-Seq by Expectation-Maximization (RSEM) values for every mucin gene in the PAAD-TCGA cohort using quantification data from RNAseq available with the cBioPortal tool. We could not retrieve any RSEM values for MUC3, MUC5AC, MUC8, MUC19, and MUC22. For the following analyses, we focused our work on available mucin data. The oncoprint is presented in Figure  3. In total, 63% of PDAC samples (105/168) harbored at least one mucin gene alteration event (amplification, deletion, mRNA high, or mutation) with MUC1, MUC17, MUC16, and MUC4 being the most frequently altered genes (15%, 15%, 14%, and 9%, respectively). There was no significant statistical difference in survival between altered and nonaltered groups (Figure  S3A ).
17 < r < -0. 44). Results of PCA confirmed that the membrane-bound mucin cluster was negatively correlated with the EMCN/MCAM cluster (Figure  4B ). These two clusters appear to be independent of MUC2, MUC5B, MUC12, and MUC13 expression as illustrated on the PCA plot (Figure  4B ). Similar analysis was conducted using the independent Queensland Centre for Medical Genomics (QCMG) pancreatic cancer dataset containing 96 transcriptomes  [4]  (Figure  S4 ). We observed a similar correlation of membrane-bound mucins MUC1, MUC4, MUC13, MUC16, MUC17, and MUC20 (0. 42 < r < 0. 64) (Figure  S4A ,B) (Table  S1 , Supplementary Materials). MUC14/EMCN and MUC18/MCAM mRNAs were also correlated with each other (r = 0. 34) (Figure  S4B ).
All these differences in expression were statistically significant (p < 0. 01). Interestingly, Kaplan-Meier curves showed that cluster #2 was associated with shorter overall survival than cluster #1 (p = 0. 05, HR = 1. 5, CI 1-2. 34) (Figure  5C ) but did not impact the progression-free survival (p = 0. 15) (not shown). Similar hierarchical clustering analysis was realized using the PAAD cohort without the four patients belonging to clusters #3 and #4. We obtained four new clusters (not shown). In total, 139 patients out of 164 were sorted into corresponding groups (84% concordance). Cluster #2 was divided into two clusters characterized by two membrane-bound mucin signatures (MUC1/13/17/20/12 or MUC4/16/20/21). An independent unsupervised hierarchical clustering algorithm on the QCMG dataset was also performed.
We also investigated the survival of PDAC patients in relation to the relative expression levels of the two clusters using SurvExpress that splits the cohort where the p-value is minimal and PROGgeneV2 that separates the two cohorts depending on the median of expression of the genes of interest. We also investigated the GSE57495 independent dataset that contains relative mRNA levels and survival of 63 PDAC patients. Using the SurvExpress tool, we observed that signature #2 was associated with a higher hazard ratio (HR = 4. 3) than signature #1 (HR = 1. 75) in the PAAD-TCGA dataset (Figure  7A ). Indeed, the high mRNA expression of MUC1, MUC4, MUC16, MUC17, MUC20, and MUC21 was correlated with shorter overall survival (p = 0. 000195) (Figure  7B ). The high mRNA expression of MUC14/EMCN, MUC15, and MUC18/MCAM was also associated, to a lower extent, with shorter survival in the PAAD dataset (Figure  7C ).
Using PROGgeneV2, we confirmed that the high expression of signature #2 was associated with a worse outcome compared to signature #2 low-expression tumors in both TCGA-PAAD and GSE57495 (HR = 1. 18-1. 45, p < 0. 001) (Figure  S5A ). On the contrary, signature #1 did not reach statistical significance in the TCGA-PAAD (p = 0. 055) or in the GSE57495 dataset (p = 0. 43) (Figure  S5B ). Altogether, this suggests that signature #2 could be useful in the stratification of patients with worse prognosis in pancreatic cancer.
MUC18/MCAM/CD146 is expressed in cancer-associated fibroblasts that are enriched in PDAC stroma  [28]. We observed for the first time a patient subgroup that is characterized by a higher expression of MUC14/EMCN, MUC18/MCAM, and MUC15 in their tumor tissue. According to Moffitt's previous work, two stromal subtypes (normal and activated) were defined and harbor independent prognostic characteristics  [6]. We hypothesize that the MUC14/MUC18/MUC15 subgroup could overlap with the different stromal populations. Recent work also showed that the expression levels of MUC14/EMCN, MUC18/MCAM, and MUC15 individually showed significant correlations with worse overall survival in stomach adenocarcinoma (STAD) from TCGA dataset  [27]  suggesting that these genes could also be prognostic biomarkers in other digestive cancers.
Expression Data Retrieval and Analysis MUC1, MUC2, MUC3A, MUC4, MUC5AC, MUC5B, MUC6, MUC8, MUC9, MUC12, MUC13, MUC14/EMCN, MUC15, MUC16, MUC17, MUC18/MCAM, MUC19, MUC20, and MUC21 expression was extracted from GEPIA  [54]. The normal (n = 171) vs. tumor (n = 179) patterns of expression were analyzed using GEPIA that recomputes the Genome Tissue Expression (GTEX) and TCGA gene expression data. The GSE28735 microarray was analyzed using the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (http://www. ncbi. nml. nih. gov/geo/). GSE28735 is a dataset containing 45 normal pancreas (adjacent non tumoral, ANT) and 45 tumor (T) tissues from PDAC cases. The mRNA expression in tumor and ANT samples was analyzed using R studio. All queries for each mucin gene were also realized in pancreatic adenocarcinoma PAAD dataset from TCGA (http://cancergenome.
nih. gov/)) and QCMG  [4]  using the cBioPortal website  [55, 56]. The mRNA expression values were retrieved as RSEM (batch-normalized from Illumina HiSeq_RNASeqV2). Gene expression and clinical information were filtered for missing values. All TCGA data analyses were processed using R studio. The complete R script is available at https://github. com/NicolasJonckheere/Mucin-script. The Pearson correlation coefficients (r-values) and p-values were calculated for each combination of mucin genes. Principal component analysis (PCA) and unsupervised hierarchical clustering (HC) were performed using the FactoMiner package in R studio. The number of clusters is automatically determined by HCPC {FactoMineR}. The default partition in four clusters is the one with the higher relative loss of inertia.
Survival Analysis Survival analysis was conducted using the SurvExpress and PROGgeneV2 online tools  [57, 58]. Individual mucin gene analysis and the signature combination analysis were performed using SurvExpress with the optimized Maximize algorithm that attributes a minimal p-value to a risk group. Hazard ratio (95% confidence interval (CI)) was also evaluated. Combinations of mucins (#1: MUC15, EMCN, and MCAM; #2: MUC1, MUC4, MUC16, MUC17, MUC20, and MUC21) were also analyzed using PROGgeneV2 in TCGA-PAAD and GSE57495 datasets. GSE57495 is an independent microarray analysis of 63 patients with pancreatic cancer tissues used to predict overall survival  [59].
Immunohistochemistry (IHC) Mucin expression was analyzed by IHC using the available scanned tumor macro array staining from ProteinAtlas database (www. proteinatlas. org/) [36]. In total, 10 to 12 PDAC samples were analyzed for MUC1 (HPA004179), MUC2 (CAB016275), MUC3A (HPA010871), MUC4 (HPA005895), MUC5AC (HPA040615), MUC5B (HPA008246), MUC6 (CAB002165), MUC7 (HPA006411), MUC9 (HPA062205), MUC12 (HPA023835), MUC13 (HPA045163), MUC14 (HPA005928), MUC15 (HPA026110), MUC16 (CAB055172), MUC17 (HPA031634), MUC18 (HPA008848), and MUC21 (HPA052028). No information could be retrieved for MUC19, MUC20, and MUC22. IHC staining was automatically determined and eventually manually adjusted by Protein Atlas experts and was indicated as high/medium/low staining or not detected.
Statistical Analysis Chi-square, ANOVA, and Student t-test statistical analyses were performed using R studio (https://rstudio. com/) and Graphpad Prism 6. 0 software (Graphpad softwares Inc. , La Jolla, CA, USA). A p-value <0. 05 was considered statistically significant. The GEPIA tool also provided t-test analysis. SurvExpress and PROGgeneV2 tools provided statistical analyses of hazard ratios and overall survival. A log-rank test was used to evaluate the equality of survival curves between the high-and low-risk groups.
Conclusions In the present work, we investigated available PDAC datasets combining mRNA expression and patient clinical features. We observed that PDAC is globally associated with an increase in the mRNA relative level of most membrane-bound mucins. We performed an unsupervised hierarchical clustering analysis that allowed us to identify a mucin signature, characterized by an increased MUC1/4/16/17/20/21 mRNA level that correlates with a worse outcome for patient survival, suggesting its potential usefulness as a prognostic marker. We hypothesize that a better knowledge of the mucin expression pattern could help clinicians for precision medicine.
For temporin-SHd, data were taken fro previous experiments  [15]  and presented as histograms for comparison with temporin-SHe. Statisti differences were assessed for temporin-SHe using one-way ANOVA followed by Dunnett's test ( < 0. 05; ** p < 0. 01; *** p < 0. 001). Mean IC50 values were determined and revealed that temporin-SHe was highly potent L. infantum (IC50 = 4. 6 µM), with enhanced leishmanicidal activity compared to its paralog tem SHd (Table  3 ).
This can be explained by the slightly higher intrinsic hydrophobicity of temporin-SHe since it is known that increasing the hydrophobic character leads to more cytotoxic AMPs  [40]. In this case, it will be interesting to modulate the hydrophobicity and amphipathicity properties of temporin-SHe in order to increase its therapeutic effectiveness  [41, 42]. Temporin-SHe represents an additional temporin with antiparasitic properties. This peptide was able to kill promastigote forms of the human parasite Leishmania, responsible for visceral (L. infantum), cutaneous (L. major) and muco-cutaneous (L. braziliensis) leishmaniasis. Compared to temporin-SHd, temporin-SHe exhibited higher antileishmanial activity (Table  3 ), particularly against L. infantum. Until today, only six temporins (Ta, Tb, SHa, SHd, Tl and Tf) have been described as antiparasitic peptides and very few AMPs of other families are active against parasites (The Antimicrobial Peptide Database, http://aps.
aureus bacteria (ATCC 25923 and MRSA strains) within infected HaCaT keratinocytes  [43]. The similar killing of MRSA cells inside HaCaT keratinocytes was also observed for an analog of temporin-CEb (formerly temporin-1CEb) conjugated with dalargin, a Leu-enkephalin analog  [44]. We have recently confirmed the intracellular activity of temporin-SHa, but this time against a bacterial pathogen, the Gram-negative Legionella pneumophila responsible for Legionnaire's disease  [31]. Temporin-SHa killed L. pneumophila bacteria within both amoebae and macrophages. A search in the database Antimicrobial Peptide Database (APD) allowed us to find the five AMPs most similar to temporin-SHe. Not surprisingly, these AMPs belong to the temporin family. In addition to temporin-SHd that shares 76. 5% identity, the four other AMPs displayed 61-68.
Differential Scanning Calorimetry Differential scanning calorimetry experiments were performed using DMPC/DMPG 3:1 multilamellar vesicles (MLVs) and different peptide/lipid molar ratios (1:200, 1:100 and 1:50), according to the procedure described in  [8]. Several scans (>20) were run for each sample with a 10 min equilibration time between each scan. The raw data were analyzed with the CpCalc software and thermodynamic values (T m and ∆H) were estimated by a peak-fitting procedure.
Statistical Analyses Statistics were determined with GraphPad Prism ® 6. 0 software (GraphPad Software, La Jolla, CA, USA). Data are represented as mean ± S. D. A one-way ANOVA followed by a Dunnett test or by a Tukey multiple comparison test was performed for comparison between the control and treated cells or for comparison between each condition (* p < 0. 05; ** p < 0. 01; *** p < 0. 001).
Acknowledgments: We thank  T. Msadek  ( Institut Pasteur Paris, France ),  J. -M. Berjeaud  ( Université de Poitiers, France ) and  S. Rebuffat  ( MNHN Paris, France ) for kindly providing the following bacterial strains: S. aureus  ST1065 ,  L. ivanovii Li4pVS2  and E. coli ML-35p/Salmonella enterica, respectively. We also thank  F. Pratlong  ( Centre National de Référence des Leishmania, Montpellier, France ) and  A. Ouaissi  ( Institut Pasteur Lille, France ) for kindly providing Leishmania strains and THP-1 monocytes, respectively, and  M. Raschetti  for technical help on the SEM Apreo S apparatus.
Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule-focused tandem mass spectrometry (MS 2 ) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS 2 dataset and to connect this chemical insight to the user's underlying biological questions. This can be performed within one liquid chromatography (LC)-MS 2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS 2 data with sample information (metadata) and annotated MS 2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset.
Molecular networking-one of the main analysis tools used within the GNPS platform-creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS 2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90-to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.
In a modified spectral similarity search, not only are fragmentation spectra (MS  2  ) from ions at identical m/z values compared, but also MS 2 spectra that are offset by the same m/z difference as the precursor ion are compared. By eliminating the amino acid filtering from the original spectral alignment algorithms, it became possible to extend spectral similarity to any set of MS 2 spectra, including those from small molecules and natural products (NPs). When a pairwise spectral similarity search/alignment is performed, each MS 2 spectrum in a given dataset is compared against every other, and a network of MS 2 spectral relations is obtained, from which molecular networks are created (Fig. 1 ). Molecular networking builds on the fundamental observation that two structurally related molecules share fragment ion patterns when subjected to MS 2 fragmentation methods such as collision induced dissociation (CID).
To make the molecular networking algorithm accessible to the scientific community, its script was converted to a web-based platform backed by a supercomputer. This enabled the creation of a community infrastructure supporting both a database and knowledge base around the needs of the community. The result was the GNPS (https://gnps. ucsd. edu/ ProteoSAFe/static/gnps-splash. jsp) community effort that started in 2014 and was published in 2016. The user base has expanded to 49 of 50 states in the United States and worldwide to >150 countries  11. GNPS is currently widely used by scientists working in industry, academia, and government in the fields of biomedical research, environmental science, ecology, forensics, microbiology, chemistry, and others. This crowd-sourced, community-driven analysis infrastructure not only facilitates data and knowledge storage but also enables knowledge capture, sharing, dissemination, and data-driven social networking while promoting reproducible data analysis.
Moreover, GNPS can be accessed on a computer or on any mobile device connected to the Internet, making any public dataset readily accessible for analysis. Although there are many analysis tools available within the GNPS infrastructure, molecular networking is the most frequently used tool. Other tools available on GNPS, such as network annotation propagation (NAP) and 'ili, which enables molecular cartography, are briefly discussed. To create a molecular network, GNPS first aligns each MS 2 spectrum in a dataset to each of the others and assigns a cosine score to each combination to describe their similarity (Fig. 1 ). Identical masses are collapsed on the basis of a hierarchical cosine clustering algorithm into a single node or consensus cluster because of the high similarity of their fragment ions.
This is accomplished using the MS-Cluster algorithm  12. Structurally related molecules yield comparable MS 2 spectra due to commonalities in their gas-phase chemistry  13  and are represented by separate nodes that connect within the network via edges. Each consensus spectrum (node) is then queried against spectral library databases to assign it to a putative known molecule within a network. All mass spectrometry data used in GNPS, both those in the private user workspace and data that are made public, are stored in MassIVE-an interactive virtual environment developed to facilitate Fig. 1  | Schematic representation of the process for creating a molecular network from tandem mass spectra acquired for metabolites in complex sample mixtures. The colors are used to track how we go from molecules in a sample to nodes in the molecular network.
We start by obtaining MS 2 spectra of all ionized molecules in the sample. MS-Cluster first aligns each MS 2 spectrum in a dataset to each of the others. Mass spectra from identical compounds are coalesced using MS-Cluster  12  into a single node or consensus cluster because of the high similarity of their precursor ion and fragment ions. Subsequently a spectral alignment is performed, enabling similarity searches even when the precursor ion masses are not identical. This is accomplished using a modified cosine score, for which all the ions that differ by the mass difference of the two precursor ions are also considered. Structurally related molecules yield comparable MS 2 spectra because of commonalities in their gas-phase chemistry and are represented by separate nodes that connect within the network via edges.
Int. , intensity. and encourage the exchange of mass spectrometry data. MassIVE accepts data files (organized as datasets) and facilitates the sharing of datasets with a unique identifier; one can use this unique identifier as an accession number for publications. In addition, public datasets that the user publishes can, by choice of the depositor, have an associated digital object identifier (DOI). Currently, MassIVE is an approved repository for the Journal of Proteome Research (https://pubs. acs. org/journal/jprobs) and Nature partner journals (https://www. nature. com/sdata/policies/repositories#chem) and is widely used as a repository for other journals  [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]. GNPS-MassIVE contains more than a thousand public metabolomics datasets. The GNPS knowledge base includes 221,083 reference MS  2   provided by the GNPS community, spectral libraries generated for GNPS (GNPS-collections), and third-party libraries  11.