dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Here, we sought to establish a link between multi-omic features that regulate the biology of GBM subtypes and protein kinases that could directly enable subtype-specific phenotypes. We built and applied SPHINKS-MK, an algorithm that integrates proteomics and phospho-proteomics datasets into a single network for the unbiased extraction of subtype-specific MKs. By informing pharmacologic and genetic experiments in subtype-matched GBM organoids, SPHINKS-MK delivered PKCδ and DNA-PKcs as experimentally validated MKs for the aggressive GPM and PPR subtypes of GBM. The four subtypes and the underlying phenotypes were also recovered across different tumor types, highlighting the fundamental biological traits that are extracted by the functional classification. In the multi-cancer context, PKCδ and DNA-PKcs have emerged as broadly actionable MKs of GPM and PPR subtypes.
Processed β-values and classification of the MNP cohort were downloaded from the Gene Expression Omnibus (GSE90496, MNP reference set) and supplementary tables published previously  9. Copy number. Thresholded CNVs were assessed using GISTIC. Protein-coding genes were retained. fCNVs were obtained as described  5. Global proteome and phospho-proteome. Values missing in <50% across all samples were imputed with DreamAI 66 and were quantile and log 2 normalized. Lipidome and metabolome. Data were downloaded as log 2 -tranformed and median normalized. Values missing in fewer than five or ten tumors for lipids or metabolites, respectively, were imputed using average abundance of the corresponding molecule. Data were quantile normalized. Acetylome. Data were imputed with DreamAI and log 2 -transformed.
27, P = 0. 017; mitochondrial enzymes: logit(NES) = -1. 19, P = 5. 93 × 10 -13 ; glycolytic intermediates: logit(NES) = 1. 76, P = 0. 0007; mitochondrial intermediates: logit(NES) = -1. 65, P = 0. 018). The network of metabolites and metabolic proteins was constructed using Ingenuity Pathway Analysis (IPA)  68. Lipid signatures included molecules with an MWW score > 0. 5. Lipids were categorized and used for enrichment of lipid subclasses, cellular components and lipid functions in each subtype using Fisher's exact test (FET; log(OR) > 0, P < 0. 05) and the lipid ontology database LION  14.
0005). Nuclear proteins were selected by the COMPARTMENTS database (nucleus score of 5). Acetyl sites with the highest variability across the dataset by interquartile range (n = 320) were used for unsupervised clustering. Differentially abundant acetyl sites in high-versus low-acetylation PPR subgroups were defined by MWW test (P < 0. 001, log 2 (FC) > 0. 3). Acetyl sites whose abundance was not explained by protein abundance were selected by comparing global protein and acetyl site abundance between high-and low-acetylation PPR subgroups using MWW test (log 2 (FC) > 0. 5, P < 0. 05). Pathway overrepresentation testing was performed using gProfiler tool (FDR < 0. 05).
Identification of subtype-specific master kinases We applied the method described previously 10 with modifications. The activity of an MK was defined as the quantification of the activation of its substrate program in each sample X i (i = 1,…,85). We binned all substrates into 25 bins according to their average abundance across all samples. For each MK, we defined {s 1 ,…,s K } the substrates in the SOPS of MK. We randomly extracted a set of n = 100 control substrates for each s k from the corresponding bin, {c 1 ,…,c 100K }. Thus, the control substrate set has a distribution of abundance levels comparable to that of SOPS, while being 100-fold larger. The activity of the MK in the sample X i was computed as: Act (X i , MK) = ∑ K k=1 ω s k × t i s k ∑ K k=1 ω s k - ∑ 100K j=1 ω c j × t i c j ∑ 100K j=1 ω c j , where ω sk and ω cj are the SPHINKS scores of the kth substrate or jth control substrate of the MK, respectively; t i s k and t i c j are the abundances of s k or c j in the ith sample, respectively.
Impact of missing values and imputation algorithm. To establish how the SPHINKS prediction of kinase-phospho-substrate interactions degrades as the level of imputation increases, we performed a set of simulations in a controlled setting where we could have a gold standard. From the CPTAC-GBM un-imputed phospho-proteomic data, we selected sites with no missing values (n = 7,302) as input for SPHINKS and generated a kinase-phosphosite interactome to be used as a gold standard. To simulate missing values, we generated new phospho-proteomic datasets by randomly replacing predefined ratios of phosphosites with missing values (r = 10%, 25% and 50%) and then imputed using DreamAI. We applied SPHINKS to predict the networks on the imputed matrices and compared them with the one reconstructed from the un-imputed matrix.
Average ΔAct (MK) for each kinase across all runs or for each run across all kinases were shown at each ratio of perturbation. Comparison of the kinase activity inferred by SPHINKS and other methods. We considered two recently reported approaches, KSEA  31  and KEA3 (ref. 32 ). We used a dataset reporting the downstream changes in phospho-protein abundance after perturbations of upstream kinase by stimulators or inhibitors  33, 34  , bringing together 24 studies encompassing 103 kinase-perturbation annotations (gold standard) for 30 kinases and 61,181 phosphosites. We employed a metric defined as 'top-k-hit' (P hit (k)), which focuses on the top k kinase predictions, as described  34  , with k = 10. To compare the kinase activity estimate among methods, for SPHINKS we considered only the validated interactions.
Additionally, we evaluated whether other approaches could identify the GBM subtype-specific kinases uncovered by SPHINKS. We applied each method on the CPTAC-GBM dataset and for each subtype derived the ranking of 129 kinases included in all five methods: (1) for SPHINKS, kinases were ranked according to the MWW score; (2) for KSEA PhosphoSitePlus and KSEA PhosphoSitePlus + NetworKIN, kinases were ranked based on the KSEA-derived z score  31  for each subtype compared to the others; and (3) for KEA3, kinases were ranked based on the MeanRank or TopRank  32  for each subtype (considering the highest 300 differentially phosphorylated proteins). For each kinase, we derived the Δ-rank as the difference in ranks between SPHINKS and any other approach (Δ-rank < 0, the rank of SPHINKS is lower, indicating higher kinase activity; Δ-rank > 0 indicates the opposite).
Development of the probabilistic classification tool for IDH wild-type GBM We used 506 tumors from the TCGA-GBM profiled by Agilent as training set as these tumors were assigned to each functional subtypes based on orthogonal validation across multiple platforms including fCNVs, somatic mutations, DNA methylation and miRNA gene signatures  5. The standardized expression of all genes from the subtype-specific signatures was used to train a multinomial regression model with lasso penalty using glmnet (α = 1, family = 'multinomial')  79. We applied a tenfold cross-validation to select the best model with the lowest cross-validation error based on the misclassification error as loss measure. As a test set (ground truth), we considered two GBM IDH wild-type RNA-seq datasets: a. TCGA-GBM cohort (n = 127) classified according to the subtyping of the matched Agilent expression tumors (ground truth); b.
The proportion of DWM invasion available through TCIA was obtained by the integration of data published previously  13  and REMBRANDT (n = 54). Quantitative radiomic features (n = 175) from 88 GBM were selected from TCIA as described  80. We performed differential analysis of radiomic data in each subtype compared to the others (FC > 0. 3, P < 0. 05; two-sided MWW test). Association between functional subtypes and radiomic subgroups from unsupervised clustering was assessed by chi-squared test.
Data availability RNA-seq expression data of the 178 FFPE-derived and 45 frozen GBM IDH wild-type are available at Synapse (http://synapse. org; accession no. syn27042663). Previously published multi-omics data from CPTAC that were re-analyzed here are available from elswhere  6, [46] [47] [48]. The human GBM transcriptomic, genomic, methylation and clinical data, BRCA and LUSC transcriptomic and clinical data were derived from the TCGA Research Network (http://cancergenome. nih. gov/) using TCGAbiolinks. BRCA transcriptomic data from METABRIC has been derived from elsewhere  63. MNP-GBM methylation data were derived from the Gene Expression Omnibus (accession no. GSE90496). Source and a subset of randomly selected unknown interactions (red dotted arrow, negative set) using kinase abundance from proteomics and substrate abundance from phosho-proteomics; (step ii) compute a score for all the interactions in the network according to the SVM classifier; (step iii) perform bagging and obtain the average SVM scores; (step iv) retain only interactions whose average score was above the average SVM score threshold (50% of the known interactions) and whose Spearman's correlation was positive; (step v) calculate MKs activity as the difference of two terms, the weighted average of the predicted substrate's abundances using the SPHINKS score as weight (left), and the weighted average of randomly selected control substrate-set (right).
Dot size indicates significance of the pathway enrichment (P < 0. 05, Fisher's exact test) and color the log 2 (FC) of the protein abundance in tumors harboring the fCNV prot alteration compared to wild-type tumors (blue to red scale indicate fCNV prot gain, red scale; fCNV prot loss, blue scale; two-sided MWW test). d-e, Chord diagram of GBM subtype assignment of the indicated classifiers in each individual tumor from TCGA (n = 199 GBM samples) (d) and CPTAC (n = 83 GBM samples) (e) datasets. f, Chord diagram of GBM subtype assignment according to the indicated classifiers in each individual tumor from the CPTAC dataset (n = 85 GBM samples). https://doi. org/10. 1038/s43018-022-00510-x Extended Data Fig. 2| Association between fCNV status of GBM driver genes and pathway-based subtypes. a, Forest plots showing the association between fCNV amplification/mutation status of GBM driver oncogenes and subtype transcriptomic activity (magenta) or abundance of protein of the corresponding gene (light blue) in the CPTAC-GBM cohort (n = 84 GBM samples; univariate logistic regression).
Rows are proteins and columns are functional subtypes (n = 82 PG-LGG samples; n = 22 PG-HGG samples). Left and top color tracks indicate functional subtypes. Unsupervised clustering was performed for each subtype-specific protein signature. For each subtype, biological pathways significantly enriched by each gene subcluster are reported on the left (P < 0. 05, Fisher's exact test). c, Kaplan-Meier curves of PG (n = 94 patients) stratified by SNF combining gene and protein signatures obtained from the functional GBM subtypes. Patients in the PPR subgroup exhibit significantly worse survival (logrank test). Functional classification of BRCA and LSCC and prognostic implications. a-b, Heat map showing the 150 highest scoring genes of the ranked lists of the four functional subtypes obtained from tumors classified in a, TCGA-(n = 810 BRCA samples) and b, METABRIC-BRCA (n = 1,088 BRCA samples) datasets (two-sided MWW test.
). Rows are genes and columns are tumors. Horizontal top and left tracks indicate functional subtypes; horizontal middle track indicates PAM50 classification of BRCA by TCGA; horizontal lower track indicates tumor grade. Unsupervised clustering was performed for each subtype-specific gene signature. Biological pathways significantly enriched by each gene subcluster are reported on the left (P < 0. 05; Fisher's exact test). c, Kaplan-Meier curves and log-rank test analysis of 1,897 BRCA patients from the combined TCGA (n = 809 patients) and METABRIC datasets (n = 1,088 patients), stratified according to the four functional subclasses (log-rank test). d, Heat map showing the 150 highest scoring genes of the ranked lists of the four functional subtypes in LUSC from TCGA database (n = 360 LUSC samples; two-sided MWW test).
Rows are genes and columns are tumors. Horizontal top and left tracks indicate functional subtypes; horizontal lower track indicates tumor grade. Unsupervised clustering was performed for each subtype-specific gene signature. For each subtype, biological pathways significantly enriched by each gene subcluster are reported on the left (P < 0. 05; Fisher's exact test). e, Kaplan-Meier curves of 356 patients with LUSC from the TCGA dataset stratified according to the four functional subclasses. f, Mitochondrial activity (NES) and menadione survival ratio (log 2 ) for 26 BRCA (upper plot) and 71 LUSC (lower plot) cell lines from DepMap. Upper track, functional classification; middle track, mitochondrial activity; lower track, menadione survival ratio. Survival ratio: difference between mitochondrial cell lines versus the others; log 2 (FC) = -1.
Acknowledgements This work was supported by  National Institutes of Health  grant nos. U54CA193313 ,  R01CA239721  and  R01CA268592  (to A. L. );  U54CA193313 ,  R01CA190891 ,  R01CA268592 ,  R01CA239698  and  R35CA253183 ;  NCI P30 Supplement GBM CARE-HOPE ; the  Chemotherapy Foundation  (to A. I. ); and the  Italian Association for Cancer Research Project IDs   21846  (IG) and 21073 (5 per mille) (to M. C. ). S. M. is recipient of a fellowship from the  Italian Association for Cancer Research. INCa-DGOS-INSERM_12560  ( SiRIC CURAMUS ) and the  Ligue Nationale contre le Cancer (LNCC; Equipe labellisée)  to M. S.
c, Dot plot showing the genes harboring fCNV prot gain or loss and relative pathway enrichment for each GBM subtype (n = 85 GBM samples). Dot size indicates significance of the pathway enrichment (P < 0. 05, Fisher's exact test) and color the log 2 (FC) of the protein abundance in tumors harboring the fCNV prot alteration compared to wild-type tumors (blue to red scale indicate fCNV prot gain, red scale; fCNV prot loss, blue scale; two-sided MWW test). d-e, Chord diagram of GBM subtype assignment of the indicated classifiers in each individual tumor from TCGA (n = 199 GBM samples) (d) and CPTAC (n = 83 GBM samples) (e) datasets. f, Chord diagram of GBM subtype assignment according to the indicated classifiers in each individual tumor from the CPTAC dataset (n = 85 GBM samples). https://doi.
Extended Data Fig. 9 | Common and specific Master Kinases across CPTAC-GBM, -PG, -BRCA, and -LSCC. Venn diagrams reporting the common and specific master kinases of each functional subtype resulted significantly activated in CPTAC-GBM, -PG, -BRCA, and -LSCC (GBM: n = 85 samples; PG: n = 104 samples; BRCA: n = 118 samples; LSCC: n = 106 samples).
Transcriptional Analysis RNA preparation and reverse transcription were performed from R. cellulolyticum derivative strains grown in minimal medium supplemented with arabinose (2 g L -1 ) or cellobiose (2 g L -1 ) as previously described  [4]. For transcriptional analysis, quantitative real-time PCR was performed using SsoFast EvaGreen Supermix 2X Kit (Bio-Rad, Marnesla-Coquette, France), and the results were analysed with Bio-Rad CFX Manager software, version 3. 0 (Bio-Rad, France) as previously described  [4].
Model Building and Docking Experiments Model building of the monomers and the complexes was performed using the whole sequence of mature proteins of CuaA and CuaD with 429 and 410 residues, respectively, using Alphafold2 and Alphafold2 multimers on the free ColabFold v1. 5. 2. server, which uses the MMesqs2 server to calculate the multiple sequence alignment. The tetrameric complex of either two CuaA or two CuaD monomers with a dimer of the truncated extracellular domain of CuaS (CuaS EC 267 residues) was calculated using an in-house Alphafold2 server with sufficient GPU memory  [20, 21]. Ligand docking was performed with the SMINA variant of the Autodock/Vina program under Windows, using an in-house created plugin (GitHub) for the PyMol structure visualization program  [22, 23]. All ligands were constructed based on monomer fragments from the PDB databank, assembled, and minimized with the free AVOGADRO program  [24].
It was shown that long cellodextrin molecules, such as cellopentaose, can fit in a large groove in the CuaA model but not in the more restricted binding pocket in the CuaD model (Figure  8 ). In the latter, only cellobiose could be docked (Figure  8 ). The best-scoring cellobiose positions occupied the same binding pocket as observed in a solved X-ray structure of the complex of a xylotriose with a similar SBP XBP1 sequence from Caldanaerobius polysaccharolyticus (pdb code: 4G68, sequence identity: 28. 3%) (Figure  S3 )  [30]. As observed for the published xylotriose/SBP complex, the cellobiose molecule establishes hydrophobic stacking interactions with aromatic amino acids, but due to an amino acid insertion in a helix in CuaD and the structural rearrangement of the following loop at the entrance of the binding pocket, this pocket is too small to fit dextrins longer than disaccharides.
Molecular profiling technologies, such as RNA sequencing, offer new opportunities to better discover and understand the molecular networks involved in complex biological processes. Clinically important variations of diseases, or responses to treatment, are often reflected, or even caused, by the dysregulation of molecular interaction networks specific to particular network regions. In this work, we propose the R package PLEX. I, that allows quantifying and testing variation in the direct neighborhood of a given node between networks corresponding to different conditions or states. We illustrate PLEX. I in two applications in which we discover variation that is associated with different responses to tamoxifen treatment and to sex-specific responses to bacterial stimuli. In the first case, PLEX.
g. , disease/health and responder/non-responder to a drug). Various methods have been proposed for the analysis of multiplex networks, many of which detect those nodes whose set of neighbors (neighborhood) is highly consistent across layers  (Buphamalai et al. , 2021; Mahapatra et al. , 2021; Peng et al. , 2021). With highly consistent neighborhoods across layers may be OPEN ACCESS EDITED BY Richard D. Emes, Nottingham Trent University, United Kingdom prioritized as the most interesting ones for downstream computational or biological analysis. This type of method is often used to aggregate networks from different omics datasets. Other methods identify nodes whose neighborhoods vary from one layer to the other. These methods are, in particular, useful for the detection of dysregulated genes that can be associated with different clinical conditions such as diseases and specific responses to treatments  (Loscalzo, 2017).
Usage data In Scenario I, we used the RNAseq gene expression data from the PRISM dataset  (Corsello et al. , 2020). We considered cell lines derived from lung cancer tumors with responses to Tamoxifen drug. We then calculated gene co-expression networks, for responders and non-responders, with 2,000 genes with the largest expression variance resulting in 2000 2 1, 999, 000 edges. As for the input multiplex network, one network layer was constructed from the transcriptomes of those cell lines that were found to respond to tamoxifen treatment (27 samples), the other was derived from those that did not (37 samples)-see Supplementary Material S2 for more details. In Scenario II, we used transcriptome measurements for healthy human blood samples before and after bacterial stimulation  (Thomas et al.
, 2015; Piasecka et al. , 2018). We considered 370 samples with equal numbers of males and females. The data contains the NanoString expression of 564 genes resulting in 564 2 158, 766 edges in each network. In the context of Scenario II, we show how PLEX. I can also be used to explore local neighborhood variations between individual specific networks (ISNs). ISNs model pairwise gene co-expression in individuals that are derived from single transcriptome measurements  (Melograna et al. , 2023). Here, we derived ISNs using the LIONESS method  (Kujjer et al. , 2019a; Kujjer et al. , 2019b). We restricted our analysis to the three bacterial stimulation of Escherichia coli, Staphylococcus aureus, and Staphylococcal enterotoxin B. For each stimulation group and each individual, we constructed a multiplex network, one layer corresponding to the stimulation case and the other layer corresponding to the unstimulated case.
Results Genes whose neighbors in GCN vary between populations with different clinical conditions appear to have a transcriptome regulatory effect, thus, are likely to explain the differences  (Van Dam et al. , 2018). In the context of personalized medicine, for instance, genes with differential co-expression patterns between drug responders and non-responders may play key roles in drug resistance. As a first usage example, we used PLEX. I to discover genes whose neighborhood variation is associated, in cell lines, with different responses to tamoxifen treatment. The input to PLEX. I consisted of gene co-expression networks, derived from cell lines of the PRISM dataset  (Corsello et al. , 2020). Using PLEX. I we identified 42 genes whose gene co-expression neighborhood varies significantly between the responsive and the non-responsive cell lines (Supplementary Table  S1 ).
(2018). A potential third scenario is to consider a set of two-layer networks, each for a pair of individuals, aiming at calculating similarities in local neighborhoods between pairs of individuals to cluster them. For future versions of PLEX. I, we plan additional features, such as the application on the multilayer networks in the third scenario mentioned above. PLEX. I is freely available under a GPL-3 license on CRAN (https://cran. r-project. org/web/packages/PLEXI/index. html) and on GitHub (https://github. com/behnam-yousefi/PLEXI). It is written in R language and can operate on any of the Windows, Linux, and MacOS operating systems. The detailed guide for implementation is available in Supplementary Material S2. Testing the code for Scenario I on the simulated network of two layers with 100 nodes (see Supplementary Material) takes 243 s and requires 203 MB of RAM on a MacOS  (version 13.
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  H2020   Marie Sklodowska-Curie Grant  Agreement No. 860895  (TranSYS; BY, FM, BS, KV), and from the  French government 's  Invest in the Future programme ; reference  ANR-10-LABX-69-01  ( Milieu Intérieur ).
1. Finite element analysis (FEA) is a computational method used to predict the behaviour (stresses, strains and deformation) of a structure under predefined loading conditions. It can be applied to different biological structures, such as bone, to study defined muscle-driven scenarios. However, as muscle is an extremely complex structure to model, evolutionary biologists usually model muscle forces indirectly. In 2007, the BONELOAD MATLAB routine was developed to distribute muscle forces on a surface defined by the user. This routine then had to be coupled with a pre-existing FEA software (e. g. Strand7) to perform the analyses and has been widely used ever since. 2. In this manuscript, we present a new method to run muscle-driven finite element simulations on a bone by distributing muscle forces on their insertions area, all within a single environment.