dataesr/scientific-paragraphs-categorization huggingface dataset:
1 we observe false positives, originating from spurious interactions. The interactions of the other four TFs (Supplementary Note 1, Supplementary Fig. 2 ) are correctly discarded by all approaches, indicating the robustness of the employed approaches (Fig. 1b ). In addition, we observed that CLR and GENIE3 demonstrated the best performance with respect to the set of known interactions. For instance, they identified the regulation of LHCSR3. 1 by CrCO  26, 27  and of PSBS1 by NF-YB  27  (Fig. 1b ). Generalization of this ranking beyond the known interactions underlying qE and CCM processes is challenging, due to the lack of genome-scale gold standard, and we, therefore, opted to combine the results of the five approaches, that showed comparable performance, in the consensus GRN (Methods, Supplementary Data 3) to increase robustness of the predictions.
By determining the intersection of the two networks, we obtained a GRN that resolves regulatory interactions underlying the transcriptomic changes observed in the phot mutant, while borrowing the statistical power of the whole RNAseq compendium. We refer to the resulting network as PHOT-specific GRN (Methods, Supplementary Data 5). Fig. 1  | Characterization of the consensus GRN inferred by employing a compendium of RNAseq data from diverse light and culture conditions. a Expression levels of representative carbon concentrating mechanism (CCM) and qE genes are plotted over all samples used for network inference (z-scaled log values are depicted, high values red, low values blue). The column annotation gives information on the culture conditions and data set (purple-Sueoka's high salt medium (HSM), green-HSM + 20 mM acetate, light gray-high light (HL), dark gray -low light (LL), black-no light) see also Supplementary Data 1); no clustering was applied.
ARACNE and Silencing columns were only plotted for comparison and were not used in building the consensus GRN (see Methods). TF1 MYB-like DNA-binding protein, Cre01. g034350; TF2 RWP8, RWP-RK transcription factor, Cre04. g218050; TF3 RWP5, RWP-RK transcription factor, Cre06. g285600; TF4 bHLH domain-containing protein, Cre07. g349152). Source data are provided in the Source Data file. When investigating the top 10 regulators of qE genes in this PHOTspecific GRN the interactions where weighted based on the importance score from GENIE3 (Supplementary Data 6, Fig. 3a ). Among the interactions in this list of PHOT-dependent regulators, we recovered two known regulators of qE, namely, ROC75  28  and CrCO  26, 27  (Fig. 3a ). These observations are in line with an existing hypothesis  24  suggesting that a CUL4-dependent E3-ligase targeting CrCO 26 acts downstream of PHOT.
Discussion The molecular actors and structure of the transcriptional regulatory mechanisms that shape Chlamydomonas' response to differential light and carbon availability are largely unknown, although they are paramount to survival of Chlamydomonas and offer valuable targets for biological engineering. Here we set out to elucidate the GRN underlying the response to light and carbon availability by combining the results from five complementary inference approaches and data from 158 RNAseq samples of cultures responding to these cues. In the network inference process for this study, we carefully choose approaches and integration procedures developed to address the inherent difficulties of RNAseq data sets (e. g. , collinearity and high number of variables compared to samples). Together with the many posttranscriptional layers of regulation in eukaryotic cells, the task of recovering the true GRN, nevertheless, remains a major challenge of the field of systems biology and this study is no exception.
11 ), and (iii) a set of regulators (Fig. 5 , Supplementary Data 10, 11) of genes putatively involved in photoprotection or CCM (Supplementary Data 8, 9), which depicted significant co-regulation at a global scale. Led by these predictions we experimentally showed that QER7 acts as a repressor of qE and CCM gene expression, LCR1 is a regulator of qE with a more expanded role on regulating CCM as previously thought and finally introduced a photoreceptor-mediated layer of regulation of CCM gene expression. These results clearly demonstrated that the generated GRN represents a powerful resource for future dissection of the transcriptional regulation of responses of Chlamydomonas to light and carbon availability. To allow easy access to this resource, we published an R-shiny webtool (https://github.
com/arendma/GRN_web) to query the networks for arbitrary regulators and target genes. We expect that the webtool will prompt more concerted, community-wide efforts in resolving the interactions between other pathways that integrate different environmental cues in Chlamydomonas. with y denoting the vector of observed expression values and y ̂the model predictions. Models with a negative coefficient of determination (R 2 ) value were discarded as regularization artifacts. The results of the remaining models were assembled into a network in which interactions were ranked by regression coefficients β normalized by the maximum absolute coefficient: e β = 1 max i f|β i |g β: ð2Þ CLR and ARACNE. The implementation of mutual information (MI)based network inference approaches from the R package minet  67  was used.
Pairwise MI was estimated based on the Spearman correlation as proposed by Olsen et al. 68. Two networks were constructed based on these MI estimates. Using the CLR approach  69  non-significant interactions were removed based on the z-scores calculated from the marginal distributions of MI values for each gene pair. Alternatively, the ARACNE algorithm  44  was used to prune the network based on the data processing inequality. For both networks only interactions originating from a TF were taken into consideration and edges were ranked according to the assigned MI value. Deconvolution and silencing. For the two networks based on decomposition of the interaction matrix G the Pearson correlation matrix obtained from gene expression values were used as input. The deconvolution approach introduced by Feizi and coworkers  70  was implemented as previously described  46.
PHOT-specific network To investigate the PHOT-specific regulatory interactions genes that are differentially expressed between phot mutant and wt under low and high light were inferred. To this end, transcript counts of genes with more than one count per million in at least four replicates from these conditions (Supplementary Data 1) were tested for differential expression using the R packages limma  71  , DeSeq2  72  , and edgeR  65. Only genes deemed significant by all three tools after Benjamini-Hochberg correction for a false discovery rate of 0. 05 were considered differentially expressed with respect to PHOT mutation. In the next step, we focused on the normalized and scaled expression levels from these differentially expressed genes and the previously mentioned conditions, to infer a PHOT-specific GRN using GENIE3.
Identification of major regulators We compiled a manually curated list of possible target genes known to be involved in the processes of qE (LHCSR1, LHCSR3. 1/2, PSBS1/2), photoprotection (Supplementary Data 8), and CCM (Supplementary Data 9). Based on the assumption that major regulators act on several genes important for a biological process, the regulatory strength of a candidate regulator (for the given process) was determined by the sum of edge weights w ij between this regulator and the k genes in the respective target gene set C TF ð Þ= X k j = 1 w TFj : ð4Þ Empirical p-value calculation using Monte-Carlo simulation The one-sided p-value for the overlap between the regulators of CCM and photoprotective genes was approximated by sampling the overlaps of random gene sets. To this end, we compiled two gene sets with the same cardinality as the curated CCM and photoprotective genes.
RY0402. 072278), qer4 (LMJ. RY0402. 202963), qer6 (LMJ. RY0402. 162350), qer7 (LMJ. RY0402. 118995). The lcr1 (strain C44), lcr1-C (strain C44-B7) and its parental strain Q30P3 as described in  17  were a kind gift from Hideya Fukuzawa. Before performing phenotyping experiments, we first confirmed that lcr1 shows no expression of LCR1 and that this is rescued in the lcr1-C strain (Supplementary Fig. 15a ). The lci8 overexpressing line was purchased from the Chlamydomonas Resource center; strain CSI_FC1G01, expressing pLM005-Cre02. g144800-Venus-3xFLAG in the CC-4533 background. Overexpression of LCI8-FLAG was verified by immunoblotting against FLAG (Supplementary Fig. 15b ). To complement qer1, a 1152 bp genomic DNA fragment from Chlamydomonas CC-4533 was amplified by PCR using KOD hot start DNA polymerase (Merck) and primers P11 and P12 (Supplementary Data 12).
RT-qPCR analysis Total RNA was extracted using the RNeasy Mini Kit (Qiagen) and treated with the RNase-Free DNase Set (Qiagen). 1 μg total RNA was reverse transcribed with oligo dT using Sensifast cDNA Synthesis kit (Meridian Bioscience, USA). qPCR reactions were performed and quantified in a Bio-Rad CFX96 system using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad). The primers (0. 3 μM) used for qPCR are listed in Supplementary Data 13. A gene encoding G protein subunit-like protein (GBLP)  80  was used as the endogenous control, and relative expression values relative to GBLP were calculated from three biological replicates, each of which contained three technical replicates. All primers using for qPCR (Supplementary Data 13) were confirmed as having at least 90% amplification efficiency.
An anti-mouse horseradish peroxidase-conjugated antiserum (Jackson Immuno Research Europe LTD) was used as a secondary antibody for 3xFLAG immunoblotting (1:10000 dilution). The blots were developed with ECL detection reagent, and images of the blots were obtained using a CCD imager (ChemiDoc MP System, Bio-Rad). For the densitometric quantification, data were normalized with ATPB.
O 2 evolution was measured using a fiber optic probe (FireStingO2, Pyro Science GmbH, Aachen, Germany). Cumulative concentration of NaHCO 3 after each addition were as follows: 25, 50, 100, 250, 500, 1000, 2000 μM. K 1/2 (Ci), the Ci concentration needed for half maximal rate of oxygen evolution, and Vmax were calculated by non-linear curve fitting to the Michaelis-Menten equation, using Prism Graph (GraphPad Software, LLC).
Data availability The in-house RNAseq data generated in this study (Supplemental Data 1) have been deposited to the GEO database under accession codes GSE227473 (phot mutant screen) and GSE227281 (HSM and acetate light stress time course). Other previously published RNAseq data used in this study  38, 39  are available in the GEO database under accession codes GSE112394 and GSE71469. The consensus and PHOT-specific GRN generated in this study are provided in edge list format in the Supplemenary Information (Supplemental Data 3, Supplemental Data 4). To allow easy access to the information, we developed an R-shiny webtool that allows to query arbitrary TFs and target genes for regulatory interactions. The R-shiny webtool can be accessed at https://github. com/arendma/GRN_web  84  Open Access This article is licensed under a Creative Commons Attribution 4.
Acknowledgements We are grateful to professor  Hideya Fukuzawa  for sending us lcr1, lcr1-C, and their respective WT strain and to  Professor Peter Jahns  for the antibody against PSBS. We thank  Michael P. Edwards  and  Qiao Wen  for their feedback and contribution to GRN_code github repository. We would like to express our gratitude towards  Arne Neumann  for implementing the R shiny application. The authors would like to thank the following agencies for funding: The  Human Frontiers Science Program  through the funding of the project  RGP0046/2018  (D. P. , Z. N. ); the  French National Research Agency  in the framework of the  Young Investigators program   ANR-18-CE20-0006  through the funding of the project MetaboLight (D. P. ); the  French National Research Agency  in the framework of the  Investissements d'Avenir program   ANR-15-IDEX-02 , through the funding of the " Origin of Life " project of the  Univ.
Grenoble-Alpes  (D. P. , Y. Y. ); the  French National Research Agency  through the funding of the  Grenoble Alliance for Integrated Structural & Cell Biology GRAL  project  ANR-17-EURE-0003  (D. P. , M. A. R. -S. ), the  Prestige Marie-Curie  co-financing grant  PRESTIGE-2017-1-0028  (M. A. R. -S. ); the  International Max Planck Research School  ' Primary Metabolism  and Plant Growth' at the  Max Planck Institute of Molecular Plant Physiology  (M. A. , Z. N. ).
Transcriptome analysis We assembled a compendium of RNAseq data (Supplementary Data 1) that capture regulation of light-dependent processes by combining inhouse produced RNAseq measurements with publicly available data from two studies of densely sampled diurnal cultures of Chlamydomonas  38, 39. For the samples in the acetate time-resolved experiment, adapter sequences were specifically trimmed from raw reads using BBduk  61  (ktrim = r k = 30 mink = 12 minlen = 50). Raw reads of the diurnal transcriptome study from Strenkert et al. 39  were obtained from NCBI GEO database (GSE112394). Reads were aligned to the Chlamydomonas reference transcriptome  62  available from JGI Phytozome (Assembly version 5) using RNA STAR aligner. The BAM files obtained from these measurements were analyzed using HTSeqcount  63  (stranded=reverse) to create raw read count files.
The raw read counts from Zones et al. 38  were obtained as. tsv from NCBI GEO (GSE71469). The final data set consists of 158 samples from 62 experimental conditions or time points (Supplementary Data 1). Genes with less than 1 count per million in at least 9 measurements where discarded and the remainder were voom  62  transformed and normalized using library normalization factors based on the TMM  64  approach as implemented in the R Bioconductor package edgeR  65.
Availability and Reproducibility of Results For the sake of total openness and transparency, all our data, results, figures, tables, and the code that generated them are available in a GitHub repository (https://github. com/vincentpmartin/sleep-content-analysis (accessed on 27 December 2022)). Moreover, to foster reproducibility, our Jupyter Notebook has been adapted for other datasets (provided that it is formatted as described in the Method and Supplemen-tary 1) and hosted in a Binder repository (https://mybinder. org/v2/gh/vincentpmartin/sleep-content-analysis/main?labpath=jupyter_notebook_sleep_content_analysis. ipynb (accessed on 27 December 2022)), giving anyone the opportunity to run our notebook online to reproduce our results or to compute the same metrics with custom data.
Whole-exome sequencing of urinary bladder UC in dogs, cats, and cows We performed whole-exome sequencing (WES) of tumor and matched normal tissue on the largest cohort of canine UC tumor-normal matched samples to date (n = 87 cases; 29 males and 58 females, representing 36 different pure and mixed breeds), and the first cohort of feline UC (n = 23 cases; 14 males and 9 females, representing 6 different breeds) and bovine UC samples (n = 8 cases from 7 females that had been grazing in pastures where BF grow; one cow having 2 independent lesions). The samples were collected from multiple institutions (n = 25) across different countries (n = 17), so as to minimize ascertainment bias and account for geographical differences. A summary of cases from these three species, their geographic location and signalment data is provided in Additional file 1: Table  S1.
V600E hotspot mutation, BRAF p. V588E (relative to the canonical transcript ENSCAFT00000006305. 5; also known as p. V595E). There is a strong breed-associated predisposition of urinary bladder UC in dogs; for example, Scottish Terriers and West Highland White Terriers have increased risk, although heritable risk factors have not yet been identified  [29, 30]. Within our canine cohort, we found that the somatic mutation of BRAF was significantly higher (p = 0. 0313, chi-squared test) in terrier breeds (19/24) compared to non-terriers  (34/63). Using the DISCOVER algorithm  [31] , we did not find any mutually exclusive or co-occurring mutated genes in the whole cohort or when comparing tumors with mutated and wildtype BRAF, which suggests that there are no differences in the driver gene landscape between these tumors.
Whole-exome bait design Agilent SureSelect bait libraries were designed for the canine, feline, and bovine exomes using gene models from Ensembl v98 and genome references CanFam3. 1  [87]  (OLIDs: 3263651, 3263641, 3263631), Felis_catus_9. 0  [88]  (OLIDs: 3261601, 3261611, 3261621), and ARS-UCD1. 2  [89]  (OLIDs: 3263141, 3263131, 3263121), respectively. Baits were designed against regions in protein-coding transcripts annotated on the main autosomes and chromosome X, with an additional 25 bp flanking each side. In the final bait design, 7. 4, 5. 1, and 7. 2% of the original canine, feline, and bovine coding regions targeted had no bait coverage. Balanced bait boosting was applied for high GC regions.
Variant calling Somatic SNVs were identified using MuTect (v1. 7)  [93]. Default parameters were used, with the exception of a minimum base quality score requirement of 30 and a maximum of three alternative alleles allowed in a matched normal sample. MAC (v1. 2)  [94]  was used to identify multi-nucleotide variants from MuTect output by identifying adjacent SNVs on the same strand. Strelka2 (v2. 9. 10)  [95]  was used to identify small indels using default parameters, with the empirical variant scoring (EVS) option disabled. Gene models and the Variant Effect Predictor (VEP)  [96]  from Ensembl v98 were used to predict the consequences of base changes and indels on proteins. The canonical transcript, as defined by Ensembl, was used to determine the variant consequence. Common SNVs, defined as variants present in 1% or more of the reference SNV databases listed below, were removed.
05. Additionally, the VAF in the matched normal was required to be < 0. 01. DISCOVER (v. 0. 9. 4)  [31]  was used to search for mutually exclusive and cooccurring somatic mutations. Catalogs of known variants in the cat, dog, and cow genomes were obtained from the 99 Lives Cat Genome Consortium (v9, from 54 cat genomes)  [88] , the National Human Genome Research Institute (NHGRI) Dog Genome Project  [97] , and the 1000 Bull Genomes Project  [98] , respectively. The VAFs from these databases were used to remove any common variants (AF ≥ 0. 01) from the somatic variant calls. Variants in the normal germline samples were identified the Genome Analysis Toolkit (GATK, v4. 2. 4. 1)  [99]. GATK HaplotypeCaller was run with a minimum base quality score 20 and softclipped bases were not used. This was followed by CombineGVCFs, GenotypeGVCFs, and SelectVariants to create a file for SNVs and another for indels.
Finally, GATK Vari-antFiltration was run, with the following options for SNVs: QC < 2, QUAL < 30, SOR > 3, FS > 60, MQ < 40, MQRankSum < -12. 5, ReadPosRankSum < -8. 0. For indels, the options were as follows: QD < 2, QUAL < 30, FS > 200, ReadPosRankSum < -20. Ensembl VEP was run, as described above, to predict variant consequences. To identify candidate risk alleles in the canine, feline, and bovine germlines, variants with a population allele frequency (AF) > 0. 001 in the corresponding variant databases (described above) were removed, along with multi-allelic sites, and sites where more than half of the samples were not genotyped. Finally, we selected variants resulting in frameshift and nonsense mutations in genes that had a one-to-one orthology with a CGC gene.
Significantly mutated genes To identify candidate driver genes, we used dNdScv (v0. 0. 1. 0, git commit ID 0633182) [101], which identifies genes under positive selection, and MuSiC2 [102], which uses multiple statistical tests to identify genes significantly mutated above a background mutation rate. The dNdScv reference databases for each species were built using Ensembl v98 canonical transcripts and only genes targeted in bait design were included. Genes with a q-value < 0. 01, when considering either substitutions only or all mutation types, were considered significant. For MuSiC2 analysis, genes with an FDR < 0. 01 from the convolution test were considered significant. The convolution test is described in the original MuSiC publication  [102].
2)  [42]  and Signal  [45]  SBS reference signatures (v2. 03; https:// github. com/ Nik-Zainal-Group/ signa ture. tools. lib/ tree/ master/ data/ RefSi gSBS_ v2. 03). SigProfilerMatrixGenerator (v1. 2. 9; [103]) was used to count the number of mutations on genic transcribed and untranscribed strands and an exact Poisson test was used to calculate significant strand bias for each mutation type. The bovine genome ARS-UCD1. 2 was first installed, with a change made to the SigProfilerMatrixGenerator script 'save_chrom_tsb_separate. py' to include a list of bovine genome chromosomes. Canonical transcripts from Ensembl v98 and exome intervals list were used.
Cell culture and chemosensitivity KU-19-19 (RRID: CVCL_1344) are a human urinary bladder urothelial carcinoma (UC) cell line [108] with low/absent intrinsic APOBEC activity  [109]. Cells used were authenticated as KU-19-19 by STR profiling and were verified as being mycoplasmafree  [110]. Cells were maintained at low passage in antibiotic-free RPMI-1640 media (Sigma, R0883) supplemented with 10% fetal bovine serum and 2 mM L-glutamine. Chemosensitivity of KU-19-19 cells to purified BFA/BFE and PT was determined by MTT assay  [111]  following continuous daily cell exposure for 3, 7, 10, and 14 days (twofold serial dilution of BFA/BFE from 79. 7 µg/ml to 0. 156 µg/ml and twofold serial dilution of PT from 100 µM to 195 nM). KU-19-19 cells were seeded in 96-well cell culture plates at 1000 cells per well in 200µL of media and incubated for 24 h at 37 °C prior to BFA/BFE or PT exposure.
Sequencing, read alignment and variant calling of the treated KU-19-19 cells NanoSeq libraries were prepared from the KU-19-19 DNA following the duplex sequencing protocol as previously described  [112]. A dilution of 0. 2 fmols was taken for amplification and sequencing to 15x coverage (x: human haploid genome equivalents) using 150-bp paired-end reads on a NovaSeq 6000. The controls (KU_PTA_0_d3, d7, d10, and d14) were sequenced from these libraries by taking 5 fmols into amplification and sequencing to 15x coverage. Sequencing reads were aligned to the human reference genome (GRCh38, including decoys and HLA) using BWA-MEM (v0. 7. 17)  [91]  and processing of the data was performed as previously described  [112]. Variant calling was done with the NanoSeq pipeline version 2 (March 24th, https:// github.
Funding This study was financially supported by grants from the  Wellcome Trust  ( WT098051 ),  Cancer Research UK  ( C20510/ A13031 ),  ERC Combat Cancer  ( 319661 ), and the  Medical Research Council  ( MR/V000292/1 ) to D. J. A. The participation of I. P. and J. P. was supported by the projects  UIDB/CVT/00772/2020  and  LA/P/0059/2020  funded by the  Portuguese Foundation for Science and Technology (FCT). S. J. A. , E. P. , and R. M. P. would like to thank the  University of Huddersfield  for financial support for this project. G. A. W. was supported by an  NSERC   Discovery Grant  ( RGPIN-2020-06472 ).
Availability of data and materials The dataset supporting the conclusions of this article is available in the European Nucleotide Archive repository (https:// www. ebi. ac. uk/ ena/ brows er/ home), under the study accession ERP142199 [113]. Catalogs of known variants in the feline genome were obtained from the 99 Lives Cat Genome Consortium (v9, from 54 cat genomes) [88]. Catalogs of known variants in the canine genome were obtained from the  National Human Genome Research Institute (NHGRI) Dog Genome  Project [97]. Catalogs of known variants in the bovine genome were obtained from and the 1000 Bull Genomes Project [98].