dataesr/scientific-paragraphs-categorization huggingface dataset:
Expression of P2RX7 and IL-18 activity are dampened in IPF patients The canonical release of IL-18 is due to activation of the P2RX7/NLRP3 pathway  (7). Since high levels of eATP are found in IPF patients  (5)  and P2RX7 is activated by such levels, it was of particular interest to investigate the involvement of P2RX7 in this disease. We used a publicly available dataset of lung homogenates from control and IPF patients (GSE47460). We found that the expression of P2RX7 is downregulated in IPF patients (Figure  1A  and B ), as well as the components of the NLRP3 inflammasome (Supplemental Figure  1 ). Since IL-18 is constitutively expressed  (37) , which partly explains the lack of difference between control and IPF patients (Figure  1B ), we investigated the signaling pathway downstream of IL-18.
Acknowledgements This research was funded by  Cancéropôle PACA , the  French Government (National Research Agency , ANR through the  "Investments for the Future": program  reference # ANR-11-LABX-0028-01 ), SJH is funded by the  " Ligue Nationale Contre le Cancer " and the " Fondation pour la recherche medicale " grant number # FDT202106013099  and  ARC  (grant number  ARCTHEM2021020003478 ). The authors also acknowledge the financial support received from the  Executive Unit for Financing Higher Education, Research, Development and Innovation (UEFISCDI), Bucharest, Romania  (grant number  PN-III-P4-ID-PCE-2020-0818 , acronym  REPAIR ). The authors wish to thank  Dr George Birchenough ,  Dr Laurent Boyer  and  Dr Bernhard Ryffel  for sharing il18 -/-, nlrp3 -/-and il1b -/-spleens respectively.
) enrolled at the Nice University Hospital since 2014. Before analysis, all samples were stored in the biobank of the Nice University Hospital, France (BB-0033-00025) approved by French Government. Written informed consent was obtained from each participant. The clinical database COLIBRI linked to the biobank was authorized by the French Data Protection Authority (CNIL authorization number #DR-2018-094) and the French ethical research committee (CPP reference number #2017 AO266251).
ICU decompensated cirrhotic patients All patients admitted to our hepatological ICU from October 2013 to September 2015 and included in our prospective cohort were retrospectively evaluated for inclusion in the study ( Figure  1  ). Inclusion criteria were the presence of a decompensated cirrhosis, admission to ICU, and having an available plasmatic measurement of PS100-Beta. Exclusion criteria were the presence of a previous neurological impairment or limitation of intensive/resuscitative measures. If several ICU admissions were available for the same patient, only the first one was considered. Cirrhosis was diagnosed based on clinical, biological, and/or anatomopathological features. Demographic data (age, gender) and natural history of cirrhosis (etiology, presence of a transjugular intrahepatic portosystemic shunt [TIPS], hepatocellular carcinoma) were collected at admission.
The area under the receiver operating curve (AUROC) was used to estimate the diagnostic value of the three models. Survival analysis was performed by using the Kaplan-Meier method and survival between groups was compared using a Log-rank test. P < 0. 05 (two-sided) was considered statistically significant. Statistical analyses were performed using JMP Pro-v16. 0 (SAS Inst. , Cary, NC, USA).
Motivations: Gene Regulatory Networks (GRN) are traditionnally inferred from gene expression profiles monitoring a specific condition or treatment. In the last decade, integrative strategies have successfully emerged to guide GRN inference from gene expression with complementary prior data. However, datasets used as prior information and validation gold standards are often related and limited to a subset of genes. This lack of complete and independent evaluation calls for new criteria to robustly estimate the optimal intensity of prior data integration in the inference process. Results: We address this issue for two common regression-based GRN inference models, an integrative Random Forest (weigthedRF) and a generalized linear model with stability selection estimated under a weighted LASSO penalty (weightedLASSO).
In this simulated null dataset, we break the link between gene expression and TFBM scores by randomly unmatching the expression profiles between regulators. A regulator then keeps its correct TFBM, but is attributed the wrong expression profile. In such a synthetic baseline, there is theoretically no joint information to be learned from the combination of expression and TFBM, and increasing data integration strength can only provide uninformative TF-gene interactions. We can then assess prediction error (Equations 6, 7) on true data relatively to this synthetic dataset at comparable EDI (Equation  9 ) values for various value of integration strength α. In order to identify the appropriate amount of TFBM knowledge to inform GRN inference, we propose that the optimal value of α for target t (hereafter denoted as αt,opt), is chosen where true prediction error is most reduced as compared to the error committed under the simulated null hypothesis.
This corresponds to a level of data integration where TFBM incorporation in the model provides a sufficient improvement of prediction over the shuffled baseline. In order to build these curves, we run weightedRF and weightedLASSO for values of α ranging from 0 to 1 with a step of 0. 1, and collect the variations of EDI and MSE depending on α. In order to capture the whole range of possible variations in the simulated null data but also the stochasticity inherent to the models, we run a large number of repetitions of weightedRF and weightedLASSO (resp. 100 and 50 repetitions) on both the true and simulated datasets. The curves M SEt(α) and EDIt(α) can then be assembled into one M SEt = ft(EDIt) curve to optimize α (see several gene examples in Figure  3  for weightedRF, and Figure  S2  for weightedLASSO).
Formally, the normalized difference in MSE between true and shuffled datasets for a value of α is measured by ∆tα ∆tα = µ MSE,shuffle (EDItα) -µ MSE,true (EDItα) σ MSE,true (EDItα) (10) with µ MSE,true (EDItα) the mean MSE at EDItα, σ MSE,true (EDItα) the standard deviation of MSE at EDItα, µ MSE,shuffle (EDItα) the mean MSE on the null dataset interpolated at EDItα, αt,opt is then the value of α that maximizes ∆tα on a certain condition: αt,opt =      0 if max α∈[0,1] (∆tα) ≤ 1 argmax α∈[0,1] (∆ tα) otherwise. ( 11 ) When max α∈[0,1] (∆tα) ≤ 1, no level of data integration seems appropriate with regard to the shuffled null data, hence αt,opt is set to 0. As an illustration, the vertical blue line in Figure  3  (c-f) indicates αt,opt in 4 gene examples.
Gene expression dataset As a case study for GRN inference, we chose the transcriptomic root response to nitrate induction in the model plant Arabidopsis thaliana  [59]. This dynamic response has the advantage of being already well characterized, and used in other previous developments to chart regulatory networks  [59, 11, 17]. Continuing efforts to uncover these regulatory mechanisms is of great agricultural interest, as nitrate is the main source of nitrogen used by most plants. Gene expression was measured in seedling roots at times 0, 5, 20, 30, 45, 60, 90, and 120 minutes after nitrate or control treatments 1. Each combination of time point and treatment was measured in three replicates, resulting in a total of N = 45 samples. The RNA-Seq raw counts were normalized via the TMM method  [48] , and lowly expressed genes were removed prior to differential expression analysis.
We selected differentially expressed genes responding to nitrate induction in time by testing the interaction terms between nitrate treatment and time modelled as natural splines, as proposed in the original article that produced this dataset  [47, 59]. A total of T = 1, 426 genes had FDR adjusted p-values under 0. 05. Among those 1,426 genes, R = 201 are annotated as transcriptional regulators. These nitrate-responsive genes and regulators are taken as input for GRN inference (Tables  S1  and S2 ).
TFBM dataset We define a promoter sequence in Arabidopsis as the sequence spanning -1,000 bp and +200 bp around gene start as defined in TAIR10, because this interval has been estimated to contain 86% of binding sites in plants  [62]. TFBM information, encoded by Position Weight Matrices (PWM), was retrieved from the JASPAR database  [14]  and The Plant Cistrome Database  [44]. Among the 201 nitrate-responsive regulators, 70 regulators were associated with a known PWM in the union of these databases. FIMO  [23]  was used to find occurences of these 70 PWMs with a significance threshold of 1e -4 in promoter sequences. When several occurences were identified in one promoter, the maximum score was kept. The TFBM occurrences within nitrate-responsive gene promoters form a prior TFBM network between TFs and targets Fig.
3 : Gene-specific data integration with DIOgene is tuned by monitoring model performance variation relatively to a synthetic null hypothesis. For the target gene AT5G48970 : a. the EDI depending on α, b the MSE depending on α, c. the MSE depending on EDI (from panels a and b). The proposed gene-specific αt,opt is the value for which the MSE is most reduced as compared to the shuffled baseline, represented as a vertical blue line (Equations 10 and 11). d,e,f : the MSE depending on EDI for three other gene examples, representing different scenarios of data integration and thus different values of αt,opt. The trends are shown for weightedRF on true data (green) and shuffled datasets where TF expression profiles were randomly unmatched from their motif (grey). For each value of α, 100 models were run and the standard deviation around the mean is represented.
At the same time, GRN obtained with DIOgene achieve near-optimal precision and recall, as compared to global α curves. In this context, we actually argue that it is desirable to tolerate sub-obtimal precision and recall results while prioritizing low MSE, for three reasons: First, the MSE is specific to the conditions and cell lines used for transcriptome collection, which is not the case of DAP-Seq, that is agnostic of environmental and cellular context. Second, it is important to note that precision and recall also increase with α in shuffled datasets, where the wrong expression profile are randomly attributed to TFs (Figures  S7  and S8 ). This illustrates that these statistics can be increased simply by enforcing data integration, even when gene expression data is uninformative. Finally, DAP-Seq and other external gold standards are often scarce and costly.
Even though this is a step toward more causality, challenges remain. One of them is the lack of a significant number of PWM, a problem amplified in non-model organisms. This limitation should be further reduced as PWM databases are completed and maintained by the community in the years to come, or as new computational methods are developed to predict binding affinities directly from DNA and protein sequences  [7]. In the meantime, almost two thirds of regulators are attributed a neutral prior value, 1 2 , in our study. The choice for this neutral prior could however be different and reflect other modelling strategies that we did not explore. Another limitation of the TFBM prior is the high chance of false positives when scanning PWM: the presence of a binding motif can occur by chance, or may not necessarily cause binding nor regulation in a cellular context.
In addition, TFBM with low complexity in PWM databases can result in hits in almost all the promoters of an organism (Figure  S4b ). In this analysis, regulators with such a widespread PWM were included, but their questionable biological relevance could lead to their exclusion or to additional weighting based on PWM complexity during GRN inference. Moreover, the PWM themselves may not be accurate priors for TF binding, due to experimental and technical biases in their identification. Finally, non canonical binding events driven by features like DNA shape, structure or repeat sequences without the need for a motif have been reported  [51] , but cannot be directly modelled in our approach. Another limitation is the computation time of the proposed optimization procedure. Because it requires the estimation of a large number of models over a complete range of α values, computation time could be reduced by several ways such as a re-implementation in C++, or through analyses estimating the minimal number of repetitions to properly assess the MSE and EDI curves (Equations 6, 7, 9).
Finally, strong levels of correlation in the input data are still hindering accurate GRN inference. A lot of pairs of regulators have correlated expression profiles. When their TFBM are different, our integration scheme can identify the relevant regulator; however when their TFBM are very similar or both unknown, identifying the meaningful one is not guaranteed. Correlation between variables also impacts the design of simulated null datasets: in our study we randomly unmatched the expression profiles of the regulators. This has the advantage of preserving the correlation structure in the expression data, thus creating a realistic null dataset. However, when many regulators have similar expression profiles, which is, to some extent, true in our case study, the simulated null data may sometimes partly resemble the original data only by chance.
Bringing more diverse expression profiles into the simulated datasets, or constraining it so that it does not contain similar versions of the true data could be envisioned. In addition to the aforementioned perspectives, the application of the proposed data integration strategy to other complex organisms is a promising lead. In this work, TFBM influencing gene expression were assumed to be located in the promoter regions of their target genes because very few distal regulations have been reported in Arabidopsis, and are still poorly understood  [35]. In organisms where regulation by distant enhancers is well documented and responsible for tissue-specificity  [4, 28] , delineating enhancer regions may be achieved through the use of additional molecular layers such as chromatin accessibility, chromatin contacts, or eQTLs.
Enhancers and promoters could then be scanned for TFBM, linked to their target genes and further guide GRN inference. In our case study, we favored the use of model prediction performance as a quality metric because it is a condition specific metric available for all genes and orthogonal to the integrated TFBM priors, which is often not the case of current experimental gold standards. Our results indicate that instead of directly minimizing prediction error as a function of TFBM contribution, the comparison to a shuffled baseline improved inferred GRN (Figure  S10 ). In essence, any inference method where data integration is tuned by a parameter could be optimized based on such a simulated null dataset. As a general guideline, we believe that both the monitored quality metric and the simulated baseline should be carefully designed in order to test a clear and relevant hypothesis for the problem at hand.
More generally, the concept of synthetic null datasets for in silico negative controls is gaining interest in genomic analyses. For example, scDEED  [61]  optimizes 2D single-cell embeddings by simulating a null data where cells similarities are broken via permutations. Similarly, clusterDE  [54]  reduces false discoveries in marker gene identification by generating a realistic null single-cell data via scDesign3  [55] , and then contrasting pipeline outputs between true and null datasets. Such synthetic controls, bearing similarities with the methodology proposed in this article, are likely to enhance rigor and causal discoveries in the field. Fig. S10 : Comparison of the proposed gene-specific data integration tuning approach, DIOgene (blue), to the simpler minimal MSE approach (red). a.
Acknowledgments This research is supported by a  CNRS   80 Prime grant  (BREAK), the  French National Research Agency , and by the  LabMUSE EpiGenMed  (R-loops project). We thank  Anaïs Baudot  for her comment on an oral presentation of this work, which inspired a part of the discussion. We are grateful to all members of the  Computational Regulatory Genomics team, Montpellier , for discussions around this project.
Data and code availability Below are the links to the data used in the course of this study. • The RNA-Seq data for the response to nitrate induction was downloaded from the GEO accession GSE97500 • The PWM used to build the TFBM dataset were retrieved from JASPAR and the Plant Cistrome Database. • To identify Arabidopsis TSSs and promoter regions, we relied on the TAIR10 GFF3 file. • The regulators of Arabidopsis used for GRN inference are the union between PlnTFDB and AtTFDB All results can be reproduced with the code available in the github repository: https://github. com/OceaneCsn/integrative_GRN_N_induction.
Intersection between the genes with αt,opt > 0 in weightedLASSO and weightedRF with DIOgene. Enrichment pvalue : 5. 9e -12 (One-sided hypergoemetric test). Precision and recall as a function of α in weightedRF, against a DAP-Seq gold standard. Precision and recall are show on true data (green) and shuffled null datasets (grey) for three network densities : 0. 005 (1432 edges), 0. 01 (2864 edges) and 0. 05 (14322 edges). The precision of the prior TFBM network of nitrate responsive genes (31956 edges, density = 0. 32) is overlaid in orange. weightedLASSO weightedRF Precision against DAP-Seq Precision against DAP-Seq weightedRF dataset dataset shuffled shuffled weightedLASSO trueData trueData Precision 0. 00 0. 20 0. 25 0. 30 0. 35 0. 40 0. 45 Fig. S6: D = 0. 005 0. 25 0. 50 0. 20 0. 25 0. 30 0.
35 0. 40 D = 0. 005 0. 75 0. 00 0. 25 0. 50 Precision 188 (16%) 1. 00 0. 00 0. 75 1. 00 0. 00 0. 25 Recall against DAPSeq 390 (34%) 572 (50%) D = 0. 01 0. 50 0. 75 1. 00 0. 00 α D = 0. 01 0. 25 0. 50 0. 75 1. 00 0. 00 0. 25 α Recall against DAPSeq D = 0. 05 0. 50 D = 0. 05 0. 75 0. 25 0. 50 1. 00 0. 75 1. 00 dataset dataset shuffled shuffled trueData trueData D = 0. 005 D = 0. 005 D = 0. 01 D = 0. 01 D = 0. 05 D = 0. 05 0. 05 0. 05 0. 20 0. 20 0. 02 0. 02 0. 04 0. 04 Recall Recall 0. 03 0. 03 0. 15 0. 15 0. 01 0. 01 0. 02 0. 02 0. 10 0. 10 0. 01 0. 01 0. 05 0. 05 0. 00 0. 25 0. 00 0. 50 0. 25 0. 75 0. 50 1. 00 0. 75 0. 00 1. 00 0. 25 0. 00 0. 50 α 0. 25 0. 75 0. 50 α 1. 00 0. 75 0. 00 1. 00 0. 25 0. 00 0. 50 0. 25 0. 75 0. 50 1. 00 0. 75 1. 00 Fig. S7: Precision and recall as a function of α in weightedLASSO, against a DAP-Seq gold standard.
Precision and recall Fig. S8: are show on true data (green) and shuffled null datasets (grey) for three network densities : 0. 005 (1432 edges), 0. 01 (2864 edges) and 0. 05 (14322 edges). The precision of the prior TFBM network of nitrate responsive genes (31956 edges, density = 0. 32) is overlaid in orange.
RNAseq and TCGA analyses For each sample, the XBP1s and RIDD activity scores were calculated using the 38 genes from the IRE1 signature  11. The expression values of the 38 transcripts were extracted from the normalized count matrix of the 67 TNBC transcriptomes from a local cohort (GSE182021) and the 45 TCGA-GBM transcriptomes. Samples were then classified as high or low for XBP1s and RIDD activities according to the median. Then CD95 mRNA expression z-score was calculated for each sample and plotted according to the XBP1s and RIDD activities in both TNBC (TN) and GBM (GB) datasets.
Acknowledgements We thank all the members of the U1242 for fruitful scientific discussions. We thank  Drs Michel Samson  and  Jacques Le Seyec  for their advice on the in vivo liver damage model. We thank the  BIOSIT H2P2  platform for immunohistochemistry, in particular  Gevorg Ghukasyan , and the  BIOSIT Animal facility ARCHE  (https://biosit. univ-rennes1. fr/). This work was funded by grants from  Fondation ARC  ( PDF20171206671 ).
A reference panel for common SNPs (MAF > 1%) was built from 1000 Genomes consortium  [22]  phase 3 data (hg38 build). When absent in 1000G, an rsid identifier was retrieved from all germline variations listed in Ensembl (http://ftp. ensembl. org/pub/release-105/variation/gvf/homo_sapiens). LD score files were computed using the ldsc. py script available at https://github. com/bulik/ldsc with the following inline options: --l2 --ld-wind-kb 1000. LD matrices for imputation were generated using the generate_genome_matrices function from the RAISS python package (see https://statistical-genetics. pages. pasteur. fr/raiss/#precomputation-of-ld-correlation). RAISS Imputation parameters were chosen to ensure high accuracy for all traits and ancestry as described in the documentation (https://statistical-genetics.
Functional annotation We mapped lead SNPs to genes through positional mapping and eQTLs (expression Quantitative Trait Loci) using Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA GWAS)  [26]. For position based mapping, SNPs were mapped to their nearest gene if the distance was under 10kb. For eQTL mapping, we selected blood For the investigation of newly detected genes in African ancestry, we focused on genes tied to SNPs discovered by JASS exclusively and belonging to at least one GeneOntology (GO) set. Then, we queried each of this gene function on The Human Gene Database GeneCards (www. genecards. org,  [27] ).
We discuss this issue in depth in the supplementary material (Supplementary note 3 and Figure  S4 ) Using the intersection of available SNPs across ancestries, we first performed a trans-ancestry multi-trait GWAS by summing the Omnibus test statistics across ancestries and by contrasting its results with a univariate trans-ancestry meta analysis (see Methods). For glycemic traits, the set of SNPs available after performing the intersection was too small (#Nsnps = 8727). Hence, the trans-ancestry GWAS was performed only on hematological traits. Focusing on hematological traits, we compared association positioning and features across ancestries, and systematically studied to what extent loci are shared across ancestries. We assessed the biological relevance of significant associations by annotating them with Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA GWAS,  [26] ) and The Human Gene Database GeneCards (www.
This suggests that the same regulatory regions are involved in gene regulation in both populations. Because of the enrichment for associated variants specific to the African ancestry population, we conducted a targeted functional analysis of the genes associated with newly identified genes in that population. 49 genes were associated with a significant lead SNP in the AFR population. Each of those genes was looked up in The Human Gene Database GeneCards (www. genecards. org,  [27] ) (Table  S17 ). Five genes mapped to hemoglobin subunits (HBG1, HBG2, HBE1, HBB and HBD) involved in beta thalassemia and fetal hemoglobin quantitative trait locus 1 diseases. Eight genes mapped to other blood-related pathways like Tubulin Beta 1 Class VI (TUBB1) expressed in platelets and CD36 molecule (CD36), a gene protein located at the platelet surface.
Genotype data Genotyping, quality control (QC) and imputation of the ConLiGen cohort has been described elsewhere  [Hou L et al. , 2016]. Brie y, DNA genotyping by array was performed from peripheral blood samples in two batches of similar composition, originally referred to as "GWAS1" (N = 1,162) and "GWAS2" (N = 1,401). Standard procedures for QC and imputation using the 1000 Genomes Project reference panel were employed. Here, we used an updated ConLiGen dataset we previously described in detail [Herrera-Rivero M et al. , 2023], in which we re-imputed the combined ConLiGen batches using the Haplotype Reference Consortium (HRC) panel. This procedure increased the number of markers and the quality of the dataset, increasing its suitability for polygenic score (PGS) analyses. Single nucleotide polymorphisms (SNPs) in 37 genes that were previously reported to contribute to Li response in ConLiGen following a gene-level genome-wide analysis  [Amare et al.