dataesr/scientific-paragraphs-categorization huggingface dataset:
To illustrate differences in phenotypes of patients infected with variants Omicron or Delta, we performed an exploratory unsupervised clustering analysis using the Kohonen's self-organized map (SOM) methodology  11  , allowing us to build 2-dimensional maps from multidimensional datasets. In a nutshell, each map is divided into districts in which patients are located by the SOM algorithm on the basis of their characteristics: patients with similar features are closely located on the maps, while patients with distinct pro les are farther from each other, hence allowing to identify key differences or similarities among them. The SOMs were obtained with the Numero package framework for the R statistical platform  12  after principal component analysis adapted for mixtures of qualitative and quantitative variables was applied (PCAMix)  13, 14.
Functional studies by this group revealed that NRP1 is likely involved in PCa metastatic migration via the upregulation of EMT genes. However, unrelated work has demonstrated that NRP1 has additional functions in development and potentially tumorigenesis  [17, 18] , some of which require PKC and AKT signalling upregulation  [19]. In this report, we confirm that NRP1 is upregulated in transition to ADT, and further, our examination of human PCa datasets suggests that it may be present in 28% NEPC tumours. Importantly, we show that NRP1 is requisite for ADT transition in in vitro studies. Mechanistically, NRP1 induces expression and activation of the PKC pathway leading ultimately to increased cell survival. Finally, specific inhibition of the PKC pathway sensitises PCa cells to chemo-hormonal treatment.
Human prostate cancer specimens Prostate tissue samples were collected as part of an Institutional Review Board approved protocol at Henri Mondor Hospital in France. This study included 169 PCa patient samples (from radical prostatectomy) without having received prior hormone treatment at the institution (HNPC) and 48 CRPC tumours (collected by transurethral resection). CRPC tumours were separated in 27 CRPC-Adeno with less than 20% of neuroendocrine differentiation and 21 CRPC-NE with more than 20% of neuroendocrine differentiation as described  [2, 8]. Immunohistochemistry of synaptophysin and chromogranin-A as NE markers were performed to attribute the percentage of NED. The study also included a few specimens derived from normal prostates from peritumoral tissues. Demographic, clinical and pathological parameters were collected prospectively in a database and retrospectively reviewed.
Analyses of single-cell RNAseq datasets ScRNAseq analysis of primary prostate tumours  [25]  was undertaken using the publicly available web tool at www. pradcellatlas. com. The epithelial atlas was explored in this work. To analyse and compare results with publicly available scRNAseq data from a castration-resistant LNCaP cell model  [26] , datasets from GSE205765 were downloaded from GEO, transformed into Seurat objects, MT levels set at 10% for each object and objects integrated using Seurat pipelines  [26]. Clusters exhibiting high mitochondrial or ribosomal signatures were considered non-viable and removed from the analysis. The Find Clusters resolution parameter was set low to generate clusters with only fundamental transcriptional differences. Cluster (c)0 included both FCS and CSS cells (33%, 52%, respectively), c1 was almost exclusively FCS (60%, 1%, respectively), c2 almost exclusively CSS (7%, 47%) as previously shown.
Statistical analysis Pearson correlations were implemented for gene-gene expression correlation using GraphPad Prism (GraphPad Software). In bar graphs and dose-response curves, comparisons between each group were performed using Student's t-test or multiple t-test. All statistical tests used a two-tailed α = 0. 05 level of significance and were performed using GraphPad Prism (GraphPad Software). For in vitro studies, graphs show pooled data with error bars representing ± SEM obtained from at least three independent experiments. Statistical significance was accepted for *P < 0. 05; **P < 0. 01; ***P < 0. 001; ****P < 0. 0001.
RESULTS Validation of the Mondor Patient dataset and identification of a neuronal transcriptional programme within the CRPC subset A neuroendocrine (NE) phenotype correlates with aggressive treatment-resistant tumours in treatment-resistant prostate cancer. Rigorous sets of signature genes have been identified, including CHGA, SYP, TUBB3, EZH2 (  [27, 28] ). To identify potential novel candidates involved in the transition from a CRPCadenocarcinoma to NE phenotype in ADT-resistant patients' tumours, whole-transcriptome profiles were analysed using oligonucleotide microarrays from 13 CRPC and 54 localised HNPC tumours (Mondor Dataset). Clinical characteristics are summarised in Table  S2. Comparative analysis between CRPC and HNPC showed a significant differential expression in 1849 genes (Heatmap, Fig.
1a  and Table  S3 ). Examination of CRPC upregulated genes, using the functional annotation tool DAVID, showed that many were involved in cell cycle, microtubule alterations, negative regulation of apoptosis as expected, as well as serine/threonine protein kinase and neuronal signatures (Fig. 1b ). Examination of the Mondor dataset for AR-regulated genes using a signature previously defined by  Hieronymus et al. , 2006 [29]  (see Table  S4 ) revealed an overall lower expression in CRPC as compared to HNPC (p = 0. 00042; Fig. 1c, left ). This result was confirmed by immunohistochemistry (IHC) with lower protein expression of androgen-responsive gene PSA in CRPC compared to HNPC tissues (two representative samples, Fig. 1c , right, and data not shown). Concomitant with the decrease in AR-targeted genes, we observed a modest increase in the CRPC-NE profile using an NEPC signature (  [2] , see Table  S5 ) (Fig.
1d ). We hypothesised that some overlapping members between a defined neurogenesis signature (GO:0022008) and the Mondor dataset might provide candidates for PCa transition to an NE phenotype. Examination of the Mondor dataset against this signature revealed upregulated expression of NRP1, NRP2, AURKA, EZH2, LAMB1, NLGN1 genes (Fig. 1e , Table  S6 ), several of which have already been identified in the NE phenotype (AURKA, EZH2, NLGN1).
NRP1 is an early induction gene for NED We decided to focus on NRP1 in NED because it has been linked to high Gleason score and ADT  [16]  and functionally linked to the regulation of PKC and AKT pathways  [19, 30, 31] , both observed in the DAVID analysis (Fig. 1b ). To validate it as a potential candidate, we observed that NRP1 expression was upregulated in the CRPC Mondor dataset (Fig. 1f , Fig. S1A ), downregulated against AR genes within the Mondor dataset (Fig. S1B ) and showed increased protein expression from HNPC to CRPC-NE stages (Fig. 1g , Fig. S1C ). To further examine the timing and location of NRP1 in PCa, we relied upon a publicly available single-cell RNAseq (scRNAseq) analysis of 13 tumour biopsies from 12 PCa patients, including those with luminal, basal, or proliferative phenotypes  [25]  for details).
To ask about NRP1 expression after ADT, we examined the SU2C-PCF dataset (208 CRPC mRNA samples across both Adeno-CRPC and NEPC (11%),  [32]. Clusterplots show NRP1 expression was upregulated in ~12% of samples (Fig. S2C ). Co-expression of NRP1 and different NE signature genes revealed that although overall co-expression against CHGA, SYP, TUB33, EZH2, TP53 showed low to negative Spearman scores, other NE genes correlated positively across the entire cohort and most coexpression analyses revealed at least some samples with positive correlation (Fig. S2C ). To better focus on NE samples only, a cohort of 39 patients (37 mRNA samples) with high NEPC and low AR scores (see Table  S7 ) was selected from the SU2C-PCF dataset. Of the 37 samples, 8 expressed NRP1 mRNA (Fig. S3 ). To try to define a true NEPC population, we examined the expression of CHGA, SYP and EZH2, all NE signature genes, and found 14 samples positive for all 3 genes.
3b ). NRP1 overexpression in LNCaP, C4-2 or 22Rv1 cells also resulted in increased PKC phosphorylation compared to control vectortransfected cells (Fig. 3c ), suggesting that PKC activation might be NRP1-dependent. In support of this, a variety of downstream PKC targets were examined during the transition from LNCaP to LNCaP-NE phenotype and in stably transfected 22Rv1 cells overexpressing NRP1 (Fig. S5 ). Both ERK and Akt exhibited increased phosphorylation and notably, apoptosis inhibitor Bcl-2 was upregulated (Fig. S5 ). PKCs form a large family, including the widely characterised isoenzymes (PKCα, PKCβ, PKCγ, PKCδ, PKCε) expressed in multiple cancers and during neuronal differentiation  [39]. Examination of the Mondor dataset showed that transcription of PRKCA and PRKCD were significantly upregulated in CRPC compared to HNPC tumours (Fig.
S6A ). Examination of a scRNAseq dataset comparing hormone-intact (FCS) and castrate-condition (CSS) LNCaP cells  [26] , GSE205765, Fig. S6B ) confirmed upregulation of PRKCA and PRKCD in CSS clusters only (See Materials and Methods for details). Upregulated phosphorylation was also observed in PKCα, PKCδ and PKCε proteins from LNCaP-NE compared to LNCaP cells (Fig. S6C ). Finally, because PKC is activated at the plasma membrane, we examined the distribution of NRP1 and isoforms PKCδ and PKCα in cytosol and membrane fractions of LNCaP and LNCaP-NE cells. High levels of EGFR or GAPDH in the membrane or cytosol fractions, respectively, showed fraction purity (Fig. S6D  left). As expected, NRP1 was upregulated in LNCaP-NE cells and primarily localised to the cell membrane. Expression of both PKCδ and PKCα isoforms was also upregulated in LNCaP-NE cells and primarily localised to the cell membrane (Fig.
S6D right). We next examined the relationship between NRP1 and PKC isotypes in NED. Cross-correlation of NRP1 and PKCD in the Mondor dataset suggested that increased transcription was tightly coupled (Fig. 3d ). Further, NRP1 transcriptional silencing in PC3 cells, which strongly expressed this protein, resulted in a significant reduction in PKCδ protein levels but did not change PKCα and PKCε levels (Fig. 3e ). Finally, co-immunoprecipitation experiments revealed that NRP1 capture resulted in co-IP of PKCδ in LNCaP-NE and PC3 cells (Fig. 3f ). Although NRP1 activation of the PKC pathway in endothelial cells requires VEGF and VEGFR coreceptors  [38] , examination of the GSE205765 scRNAseq dataset found none of these mediators in either FCS or CSS condition (data not shown). Taken together, these results indicate that NRP1 positively regulates PKC expression and activation in NED and may directly associate with specific isoforms in the cell membrane.
The NRP1/PKC axis confers resistance to taxane chemotherapy We have previously reported that CRPC cells with upregulated NE phenotype are resistant to a wide range of cytotoxic agents, including taxane chemotherapies  [34]. Moreover, analysis of RNAsequencing data of 150 metastatic CRPC bone or soft tumour biopsies from the Robinson cohort  [43]  showed a trend toward increased expression of NRP1 in patients treated with taxane chemotherapies (Fig. 5a ). In vitro, docetaxel treatment of LNCaP cells induced the expression of NRP1 in cells that survive following treatment (Fig. 5a ). These results strongly suggest a role for NRP1induced NED in resistance to docetaxel. To answer this question, LNCaP-NRP1 and LNCaP-vector cells were treated for 72 h with docetaxel and cell viability was measured by MTT.
DISCUSSION Our results showed that NRP1, a transmembrane glycoprotein expressed in a wide variety of human cancers, and required for aggressive tumour growth and tumour-related angiogenesis (reviewed in ref. [49] , is an important early player in PCa drug resistance and a putative candidate for its induction. Our analyses of expression profiles from human PCa specimens (Mondor dataset) identified NRP1 as a central feature amongst the CRPC cohort overexpressed NE-related genes. We found that NRP1 was also detectable in HTPC-adeno and CRPC-Adeno specimens (data not shown) and primary luminal tumour samples  [25] , indicating that low NRP1 expression precedes transition to drug resistance. In agreement with others  [16] , we also observed that androgens maintain NRP1 at low levels and that release from their effects promotes strong upregulation of both NRP1 transcription and protein expression.
Importantly, our NRP1 overexpression and knockdown studies showed for the first time that NRP1 may be a key player in the transition to therapy resistance. Its upregulation during ADT transition and potential role(s) in the induction of ADT-NE indicate NRP1 as a novel biomarker and a target for more efficacious therapy to prevent PCa drug resistance. s iC T L s iP K C α s iP K C δ s iP K C ε s iC T L s iP K C α s iP K C δ s iP K C ε s iC T L s iP K C α s iP K C δ s iP K C ε 0 Our examination of the human SU2C-PCF CRPC dataset revealed that NRP1 and downstream PKC players are expressed in a subset of NEPC tumours. Others have observed plasticity in advanced PCa associated with epigenetic reprogramming driven by N-Myc  [50]. Thus, multiple NEPC subtypes likely exist reflecting this (and potentially other unrelated) epigenetic transitions.
Further, both NRP1 overexpression and knockdown studies support the notion that NRP1 upregulation during ADT is directly required for PKC activation. Work in unrelated fields has shown that NRP1 directly associates with VEGFRs for VEGFmediated PKC induction during angiogenesis  [38]  We have not identified either VEGFRs or VEGF in our cultures (data not shown), but NRP1 is a well-recognised pleiotropic receptor and has been shown to associate with plexins, PDGFRs, leptin, etc. to mediate numerous downstream functions  [52, 53]. Identification of specific NRP1 co-receptors and ligands for PKC induction in drug-resistant PCa is a current focus of our lab. Microarray data from the patient cohort (Mondor dataset) showed that PKCα and PKCδ were most highly expressed in human CRPC, and our in vitro results and examination of a scRNAseq database  [26]  confirmed this finding.
ACKNOWLEDGEMENTS We thank gratefully  Patricia Zadigue  ( INSERM U955 , Equipe 7,  UPEC), Alexandre Fifre (CRRET Laboratory, UPEC)  for providing advices and technical assistance. We thank  Karine Mahéo  for her scientific and technical supports for studying PKC pathway (UMR,  INSERM U 1069 , Tours). We thank  Richard Souktani  and  Cécile Lecointe  who are in charge of the Small Animal Functional Exploration Platform and IMRB (INSERM,  U955 )  Pet Shops ,  Aurélie Guguin  and  Adeline Henry  for the Flow Cytometry facility of IMRB (INSERM,  U955 ).
The conventional methods to detect homologous protein pairs use the comparison of protein sequences. But the sequences of two homologous proteins may diverge significantly and consequently may be undetectable by standard approaches. The release of the AlphaFold 2. 0 software enables the prediction of highly accurate protein structures and opens many opportunities to advance our understanding of protein functions, including the detection of homologous protein structure pairs. In this proof-of-concept work, we search for the closest homologous protein pairs using the structure models of five model organisms from the AlphaFold database. We compare the results with homologous protein pairs detected by their sequence similarity and show that the structural matching approach finds a similar set of results.
Introduction For the last decades the field of similarity searching between proteins has been dominated by sequence similarity methods. This is due to the vast numbers of available protein sequences in databases such as UniProt  [1]. With the availability of highly accurate structural models for all proteins  [2] , structural similarity may begin to supplant or enhance sequence search for some applications. In this manuscript, we investigate the use of structural similarity search to identify homologous protein pairs between human and four model organisms. Identification of homologous functioning proteins between a human and a model organism can help researchers connect experimental data across species and identify relevant model organism proteins and genes for the study of human disease. There are numerous examples where the identification of these connections has advanced molecular biology.
Methods We used data from the UniProt Reference Proteomes for the following organisms: Homo sapiens (human), Caenorhabditis elegans (worm), Drosophila melanogaster (fruit fly), Saccharomyces cerevisiae (budding yeast) and Schizosaccharomyces pombe (fission yeast)  [1]. We selected this set of organisms to investigate phylogenetically distant but well-studied species across the opisthokonts.
Protein sequence and structure data sets We downloaded the AlphaFold structural models for the five selected organisms from the AlphaFold Protein Database (release v2)  [12]. Proteins longer than 2,700 residues are only available as multiple fragment entries in the AlphaFold database and were ignored for this study (208 Homo sapiens proteins). We extracted the sequences for each of the available structure models in Fasta format. Thus, our data set contains 20,296 protein sequences and structure models for Homo sapiens (H. sapiens), 19,694 for Caenorhabditis elegans (C. elegans), 13,458 for Drosophila melanogaster (D. melanogaster), 6,040 for Saccharomyces cerevisiae (S. cerevisiae) and 5,128 for Schizosaccharomyces pombe (S. pombe).
The results show that the coverage threshold yields a higher precision (Table  1 ). Additionally, we tested different coverage thresholds for both the sequence and structural matching approach (Suppl. Figure  S2, S3 ). However, not every protein has a PANTHER classification, with this in mind, it is necessary to investigate whether the RBSH found without the same PANTHER family classification are incorrectly detected by the RBSH method or are novel homologous protein pairs, which still need to be classified in the PANTHER database. Overall, we see that in each case there are more putative closest homologous pairs found with the structure based method compared  The fraction of matches in common between the RBH and RBSH methods was not as high as we expected, with many homologues differing between the two approaches.
Novel distant homologues To better understand the performance of the RBSH approach we investigated the best reciprocal hits exclusively identified by the RBSH method. To support that these matches share common characteristics and possible function, we conducted two analyses, (i) identification of their protein families using the PANTHER classification system and (ii) comparison of their annotated protein domains using the Pfam database  [24]. The results show that this set of proteins contains many RBSH with the same PANTHER family classification or the same Pfam domain annotation (Figure  4 ). The fraction of RBSH that are uniquely identified but have no support   We further analysed the RBSH without a common Pfam domain annotation nor PANTHER family classification. These matches would be the potentially most important, representing completely novel homologues identified by the use of structural matching, which can help to understand the function of uncharacterised proteins.
Indeed, many of the RBH exclusively found by BLASTP have an average length below 200 residues (Suppl. figure  S7 ). pombe (UniProtKB:O13825). The structure images were produced using Chimera and the sequence alignment was performed using Jalview  [27, 32]  Discussion Protein sequences encode the information about protein structures and functions, and the use of protein sequences for the detection of closest homologous protein pairs is widely established  [14, 33]. The release of the AlphaFold 2. 0 structure predictor gives us now the opportunity to use highly accurate protein structure predictions to find the closest homologous protein pairs  [34, 12]. A structural matching approach is expected to detect novel homologues, which were previously missed due to sequence divergence. In this study, we assess the detection of homologous protein pairs using protein structures in comparison to protein sequences.
The comparison between the two approaches is therefore limited by the scoring discrepancies. Lastly, we also found protein pairs exclusively detected by the RBSH or RBH approach. We used the PANTHER classification system to validate the RBSH and RBH approaches. Even though we found a higher number of RBSH compared to RBH, the validation showed that the conventional RBH approach yields a higher precision score compared to the RBSH approach. This outcome can be explained by limitations of the RBSH approach, but also possibly due to the bias that homologues were so far mainly detected using the protein sequences and protein pairs with similar sequences are therefore better studied and more prevalent in the PANTHER database. It is likely that the PANTHER database aligns well with RBH because it is also a sequence based method  [16].
Even for S. pombe a species with extensive coverage of curated orthologues the RBSH approach provided 39 novel S. cerevisiae and 11 novel human homologues, which were added to the PomBase database (Suppl. Tables S5, S6)  [36]. Most of these novel connections provided additional functional information, or supported existing knowledge for poorly characterised proteins. We believe that the RBSH approach can advance the understanding of protein functions across the model organisms and also for many other species, as for example, emerging pathogens  [37].
In their conclusions, with which we concur with some exceptions discussed below, the genera Tatochila, Piercolias, Hypsochila, Theochila, Pierphulia, and Infraphulia were all treated as junior subjective synonyms of Phulia Herrich-Schäffer. Also, in the same paper, for the first time, molecular data were included for Piercolias and the monotypic Reliquia Ackery, 1975, endemic to the Colombian Sierra Nevada de Santa Marta, with the latter newly regarded as a junior subjective synonym of Pontia [Fabricius], 1807. The discovery of two new species described in this paper, and the uncertainty regarding their generic classification, inspired us to revisit the systematics of high-altitude Neotropical Pierina (with the exception of the loosely related genus Leptophobia, Butler, 1870, which comprises several mid to high-altitude Andean species, belonging to the sister clade of Pierina including non-neotropical genera, such as Pieris Schrank, 1801), with the benefit of both morphological and molecular data and a more comprehensive taxon sample than included in previous studies.
The following PCR cycling profile was applied for COI: 95 °C for 5 min, 40 cycles of 94 °C for 30 s, 50 °C for 30 s, 72 °C for 1 min 30 s, with a final extension period of 72 °C for 10 m a. s. l. in; and for both nuclear genes: 95 °C for 5 min, 40 cycles of 94 °C for 30 s, 55 °C for 30 s, 72 °C for 1 min 30 s, and a final extension period of 72 °C for 10 m a. s. l. in PCR products were sent for purification and sequencing to Macrogen Europe (Amsterdam, Netherlands). Additional sequences of 13 related species were imported from GenBank and BOLD System databases: Phulia nymphula (Blanchard, 1852), Pierphulia rosea  (Ureta, 1956) , Infraphulia ilyodes  (Ureta, 1955) , Tatochila autodice (Hübner, 1818), Tatochila mercedis (Eschscholtz, 1821), Tatochila homoeodice  (Paravicini, 1910) , Tatochila theodice (Boisduval, 1832), Theochila maenacte  (Boisduval, 1836) , Hypsochila wagenknechti  (Ureta, 1938) , Hypsochila microdice (Blanchard, 1852), with Ganyra josephina (Godart, 1819), and Ascia monuste (Linnaeus, 1764) as outgroups.