dataesr/scientific-paragraphs-categorization huggingface dataset:
The data were analyzed with OriginPro 9. 0 (OriginLab). Fluorescence sensitivity experiments. Hypocrates sensitivity experiments were performed in 100 mM sodium phosphate buffer (pH 7. 4). Aliquots of the purified protein (0. 5 µM) were incubated with increasing concentrations of oxidants (NaOCl, NaOBr, HOSCN, and N-chlorotaurine) for 5 min at 25 °C. The excitation scans (with λ em = 555 nm) were recorded with the use of a SpectraMax iD5 plate reader (Molecular Devices) controlled by Softmax Pro 7. 1. The Ex 500 /Ex 417 ratio, which represents the ratio between fluorescence excited at 500 nm and at 417 nm, was plotted as a function of increasing oxidant concentration. The initial linear part of the hyperbolic curve was analyzed using linear regression, where the slope values represent the sensitivity towards the corresponding oxidants.
For each measurement, at least two independent replicates were performed, and the data were analyzed using GraphPad Prism8 and OriginPro 9. 0 (OriginLab). The sensitivities of Hypocrates at different pH values towards N-chlorotaurine were analyzed in 100 mM sodium phosphate buffers (pH 7. 00, 7. 30, 7. 60, and 7. 90). Aliquots of the purified protein (0. 5 µM) were treated with serial N-chlorotaurine additions of 0. 3-5 µM up to the final concentration of 15 µM. The fluorescence excitation spectra (λ em = 525 nm) were recorded with the use of a Varian Cary Eclipse Fluorescence Spectrophotometer controlled by Cary Eclipse Scan 1. 1(132) Application. At each titration step, the samples were mixed by pipetting prior to the final spectra registration until the signal stabilization was observed.
The Ex 500 / Ex 425 ratio, which represents the ratio between fluorescence excited at 500 nm and at 425 nm, was plotted as a function of increasing oxidant concentration. For each buffer, three independent replicates were performed, and the data were analyzed using OriginPro 9. 0 (OriginLab). Pre-steady-state kinetic measurements. Pre-steady-state kinetic measurements were performed using a stopped-flow apparatus coupled to a fluorescence detector (Applied Photophysics SV20) and controlled by Prodata SX 2. 5. 0. For HypR, changes in intrinsic Tyr fluorescence were measured using a > 305 nm cut-off filter (λ ex = 274 nm). For NemR C106 , changes in intrinsic Trp fluorescence were measured using a > 320 nm cut-off filter (λ ex = 295 nm). For Hypocrates, changes in the cpYFP chromophore fluorescence were measured using a > 515 nm cut-off filter (λ ex = 485 nm).
For MS1 scans, the m/z scan range was set from 350 to 1800. The recorded raw data were inspected with Freestyle 1. 6. The MS/MS data were processed using Sequest HT search engine within Proteome Discoverer 2. 4 SP1 against a Escherichia coli K12 protein database obtained from Uniprot (4349 entries) and containing the Hypocrates sequence. Trypsin was specified as the protease allowing up to 2 missed cleavages, 4 modifications per peptide, and up to 5 charges. The mass error was set to 10 ppm for precursor ions and 0. 5 Da for fragment ions. Oxidation on Met and Trp (+15. 995 Da), dioxidation on Cys and Trp (+31. 990 Da), trioxidation on Cys (+47. 985 Da), bromination or chlorination on Phe, His, and Trp (+77. 991 and +33. 961 Da, respectively) and N-ethylmaleimide on Cys (+125. 048 Da) were considered as variable modifications.
False discovery rate (FDR) was assessed using Percolator and thresholds for protein, peptide, and modification site were specified at 1%. For abundance comparison, abundance ratios were calculated by Label Free Quantification (LFQ) of the precursor intensities within Proteome Discoverer 2. 4 SP1. The mass spectrometry data have been deposited to the ProteomeXchange Consortium via the PRIDE  65  partner repository with the dataset identifier PXD029624 and https://doi. org/10. 6019/PXD029624. HypocratesCS crystallization, X-ray data collection, and structure determination. HypocratesCS was crystallized at 7 mg/ml concentration at 10 °C or 20 °C using the hanging-drop vapor diffusion method with Tris (0. 1 M, pH 8), CaCl 2 (0. 1 M), MgCl 2 (0. 1 M), and PE15/4 (15%) as a precipitant solution.
The drops were composed of 1 µl of protein and 1 µl of precipitant solution. To obtain larger and better diffracting crystals, the small needles obtained within the above crystallization condition were used for microseeding. For X-ray data collection, the cryo-protectant used was the same as the precipitant solution, but with 30% PE15/4. X-ray data were collected at 100 K at the Proxima 2 beamline of the Soleil synchrotron facility, at a wavelength of 0. 980113 Å, and processed using XDS 0. 6. 5. 5  66. The structure of HypocratesCS was solved by molecular replacement using Phase 2. 7. 16  67  from the Phenix suite  68  , using both the E. coli NemR (PDB: 4YZE [https://doi. org/10. 2210/pdb4YZE/pdb]) (100% identical to the sensory domain of HypocratesCS) and the cpYFP-based calcium biosensor (PDB: 3O77 [https://doi.
4 kDa), carbonic anhydrase (29 kDa), bovine serum albumin (66 kDa), alcohol dehydrogenase (150 kDa), b-amilase (200 kDa) and ferritin (450 kDa). The equipment was controlled by Agilent OpenLAB CDS ChemStation Edition C. 01. 07 SR3 software. Hypocrates samples were incubated in the presence of 5 mM DTT or with N-chlorotaurine at 1:20 protein/oxidant ratio for 5 min before the injection (50 µl). The sensor was visualized by measuring absorbance at 415 nm to detect only molecules with the mature chromophore. The data were analyzed with Ori-ginPro 9. 0 (OriginLab). Measurements of MPO activity with purified Hypocrates. To test whether Hypocrates is capable of visualizing MPO activity in vitro, the purified sensor was incubated with 0. 1 U/ml human MPO and 100 μM H 2 O 2 for 10 min in PBS, after which the fluorescence excitation spectrum (λ em = 525 nm) was recorded with the use of a Varian Cary Eclipse Fluorescence Spectrophotometer controlled by Cary Eclipse Scan 1.
Acknowledgements The work was supported by the  Russian Foundation for Basic Research (RFBR)  Grant  18-34-20032  (to D. S. B. ); RFBR and Moscow city Government according to the project  № 21-34-70031  (to D. S. B. ); the  Russian Science Foundation (RSF)  Grant  17-15-01175  (to D. S. B) for work related to the preparation and testing of Hypocrates biosensor in the eukaryotic system; the  Ministry of Science and Higher Education , grant  075-15-2019-1933  (to A. I. K. , A. S. P. , D. S. B. , and V. V. B. ); RFBR Grant  18-29-08049  for gel filtration experiments (to A. Yu. G. ); the Grants from the  Vlaams Instituut voor Biotechnologie  (to J. M. ); the  Research Foundation-Flanders  grant for the anaerobic workstation no. 1508316N  (to IVM); the  Research Foundation-Flanders-Fonds de la Recherche Scientifique Excellence of Science  project no.
Data availability The X-ray crystal structure of HypocratesCS was deposited in the protein data bank under accession code 6ZUI. The entry has been assigned the following PDB DOI: [https://doi. org/10. 2210/pdb6ZUI/pdb]. The mass spectrometry data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD029624 [https://doi. org/10. 6019/PXD029624]. The additional raw data files underlying the Source Data are available upon reasonable request from the corresponding authors, requests will be answered within 2 weeks. Source data are provided with this paper.
2 2Fig. 1 21 Fig. 1 Hypocrates (NemR-cpYFP biosensor) design and spectral characteristics. a The structure of NemR C106 (PDB ID: 4YZE) shows the N-terminal DNA-binding domain (colored blue), the C-terminal sensory domain (colored cyan), Cys106 and Lys175 (colored yellow), and the flexible loop (colored red) into which cpYFP was inserted. The N-and C-termini are indicated with N and C, respectively. b The proposed simplified scheme of NemR-cpYFP biosensors functioning in living cells. c The structure of Hypocrates is presented with NemR C106 colored blue/cyan, cpYFP colored yellow, the linkers between NemR C106 and cpYFP colored green, and the flexible loop colored red. The upper numbers represent amino acid numbering corresponding to the intact NemR C106 , while the lower numbers represent numbering corresponding to the biosensor.
Genetic Samples and Analysis We used the CLOZUK1 and CLOZUK2 cohorts as our primary source of individuals with treatment-resistant symptoms, with a total sample size of 10 501 individuals with TRS and 24 542 controls. These cohorts have been described in previous studies. 28, 33 All the individuals with TRS in these samples were prescribed clozapine in the UK after failure of at least 2 trials of antipsychotics, following National Institute for Health and Care Excellence guidelines for TRS. 34 The use of a history of taking clozapine as equivalent to a research diagnosis of TRS has been validated in these samples,  28  as well as in independent studies. 35, 36 Control individuals were collected from public databases or through collaboration with population sequencing projects in the UK. To identify individuals with non-TRS, we used 34 studies included in the meta-analysis by the Schizophrenia.
Key Points Question Can common genetic variants be used to differentiate between treatment-resistant schizophrenia (TRS) and other forms of this disorder? Findings Data from this genome-wide association study including 85 490 participants were used to estimate genome-wide single-nucleotide variation effect size differences between individuals with and without TRS, which were compatible with a polygenic model of treatment resistance. Results were used to generate a polygenic risk score, which was significantly associated with TRS status in independent incidence and prevalence samples. Meaning Findings of this study based on common genetic variants indicate that TRS is heritable with a modest but significant single-nucleotide variation-based heritability. Working Group of the Psychiatric Genomics Consortium (PGC),  27  with a total sample size of 20 325 individuals with schizophrenia and 30 122 controls.
In 14 of these studies, available clinical records allowed us to identify and remove all individuals with TRS (eMethods in Supplement 1). The remaining 20 studies were not screened as comparable data were not available, and thus, we conservatively included these samples in the analysis as non-treatment-resistant cases (eTable 1 in Supplement 1). Healthy control individuals in this analysis were a mixture of publicly available samples and clinically ascertained (nonpsychiatric) individuals, and extensive analyses to discard population outliers and assess stratification were also carried out. 27 WASs of individuals with TRS vs controls and individuals with non-TRS vs controls were carried out separately using the logistic regression model implemented in PLINK version 2. 37  Further details on the imputation, quality control, and association testing procedures are described in the eMethods in Supplement 1.
Statistical Analysis Comparing the TRS and Non-TRS Association Studies To generate association statistics that reflect TRS vs non-TRS differences, we used the test for interaction proposed by Altman and Bland,  38  which is analogous to a fixed-effect test for moderators in the meta-analytic setting. 39 This test allows us to calculate an estimate of the difference between 2 odds ratios (ORs), which in our case were those of individuals with TRS vs controls (OR 1 ) and those with non-TRS vs controls (OR 2 ). The difference, in the scale of the regression β coefficient, equates to difference = log(OR 1 ) -log(OR 2 ), with standard error SE(d) = ͙(SE[OR 1 ] 2 + SE[OR 2 ] 2 ). We transformed this effect size into a z score and calculated its associated P value at each overlapping single-nucleotide variation (SNV; formerly single-nucleotide polymorphism) between the TRS and non-TRS GWAS, excluding 146 SNVs with a minor allele frequency difference greater than 20% between both data sets to mirror preimputation quality control.
44 We also used a new multiancestry incidence cohort of people with first-episode psychosis, the STRATA-G consortium, in which the participants have been followed up for at least 1 year after initial presentation to ascertain diagnosis and treatment response (eMethods in Supplement 1). As both the first-episode and broad psychosis nature of this sample likely make it diagnostically heterogeneous, we first restricted our analyses to individuals with a diagnosis of schizophrenia at the last time of follow-up. TRS was rated as the presence or absence of a history of taking clozapine (n = 562; 71 with TRS and 492 with non-TRS) to best mirror our TRS GWAS and the definition used in the prevalence cohort. Further details about the recruitment and phenotyping of the STRATA-G sample as well as details of the genotyping and imputation procedure for both data sets are given in the eMethods and eTable 2 in Supplement 1.
TRS/Non-TRS GWAS The study included a total of 85 490 participants (48 635 [56. 9%] male) in its GWAS stage and 1380 participants (859 [62. 2%] male) in its PRS validation stage. Discovery GWAS of individuals with TRS and non-TRS against independent controls showed highly consistent genome-wide significant signals (eFigure 1 in Supplement 1), all reported in previous schizophrenia studies. This consistency extended to most of the polygenic architecture, as shown by a high genetic correlation estimate between the data sets (r = 0. 966; SE, 0. 037; P = 1. 43 × 10 -147 ) in which the 95% CI includes 1. No genomewide SNVs were detected by the TRS interaction analysis (Figure  1 ), although a slight departure from normality was detected in the genome-wide Q-Q plot (λ = 1. 062; λ 1000 = 1. 002; LD-Score intercept, 1.
Implications In revealing the existence of a common, heritable genetic signal for TRS, our study adds a new layer of evidence to the ongoing discussion of whether TRS is categorically distinct from treatment-responsive schizophrenia. 17 Despite a large genetic correlation between both conditions, we show that PRS derived from them perform differently when attempting out-of-sample prediction of TRS, which suggests that the heterogeneous results previously obtained in PRS analyses might have been influenced by the presence of individuals with TRS in the generic schizophrenia training sample. 19 While follow-up studies would likely require rich clinical data on large numbers of individuals with TRS and non-TRS to fully explore the implications of these results, they pose an important consideration for research seeking to understand whether  the genetics of disorder susceptibility are the same as disease course in schizophrenia 2 and whether treatment response can be influenced by the accumulation of genetic and clinical factors.
Conclusions In this GWAS, TRS had a small but detectable heritability associated with common risk alleles. Validation work, via the PRS method, showed that the contribution of these alleles were similar across incidence and prevalence samples. This is despite differences in cohort characteristics, and the use of drug prescription data as a proxy for TRS instead of quantitative metrics. Altogether, these results highlight the usefulness of well-controlled clinical phenotype data in psychiatric genetics to explore beyond diagnostic classifications and into treatment outcome and response to aid precision psychiatry.
This led to the identification of more than 150,000 previously undescribed transcripts that were used together with available protein-level annotations to analyze mass spectrometry (MS)-based proteomics data. To understand the cellular signaling events that govern encystment, we also analyzed the phosphorylation site regulations occurring in the same conditions by mass spectrometry. This resulted in a comprehensive molecular atlas of the early steps of A. castellanii encystment. These transcriptomic, proteomic and phosphoproteomic data sets provide a valuable overview of the regulation events that occur during the early steps of FLA encystment. We believe that the community can build upon this work to improve proteome annotation of A. castellanii and find molecular drivers of encystment.
Control cells were grown in nutrientcomplete growth medium (PYG), in contrast to the nutrientdevoid starvation tris-based medium used to induce encystment. We estimated the reference genome completeness to be 77% (Table  1 ). Thus, to increase coverage and identify new transcripts, we generated a transcriptome de novo for which we obtained a completeness score of 94% (Table  1 ). This transcriptome contained 166,782 transcripts for 55,723 annotated genes (Table  2 ). This number is much higher than the 15,455 genes already reported  5. Such a difference could result from an overestimation of the number of transcripts assembled by Trinity due to non-coding RNAs or isoforms. We used the 166,782 identified transcripts to perform a InterProScan analysis that resulted in 121,821 matches, whereas the BLASTx (nonredundant protein database) search yielded 145,978 hits (Fig.
1 , green pipeline). The InterProScan analysis of our transcriptome suggested the presence of 26,391 protein domains against 41,572 genes using BLASTx. Mapping these outputs to the mass spectrometry data described in the next sections allowed us to focus on detectable proteins in the next parts of this manuscript. We explored the relative transcript abundances during encystment. DESeq2 identified 16,733 differentially expressed transcripts (false discovery rate ≤ 0. 001; 8542 genes according to the Trinity annotation)  26. These were clustered using Weighted Correlation Network Analysis (WGCNA)  27  , which resulted in 19 expression modules containing transcripts presenting similar regulation patterns during encystment (Supplementary Data 1). Figure  2a  and Supplementary Fig. 2  present the mean log2transformed fold change relative to T0 and independent transcript regulations, respectively.
castellanii strain Neff  5  , which we completed with protein sequences identified from the transcriptome (Fig. 3a ). With this custom database we identified 8577 protein groups (proteins that cannot be distinguished by MS because identified by a common set of peptides) (Fig. 3a ) among which 2342 had no functional annotation retrieved with eggNOG-mapper. 5129 protein groups were present in both our transcriptome and the proteome of reference, 747 were only identified in the reference proteome, and 2701 were specifically identified from sequences of the transcriptome of A. castellanii encystment. This highlights the importance in combining context-specific transcriptomic data to analyze the proteome of poorly annotated organisms. Proteins were analyzed together with their most intense transcript to get a combined view of RNA-and protein-level regulation.
As previously mentioned, few proteins showed regulation in the control condition, except in the clusters 4, 9, and 10, which contained altogether 48 protein groups. During encystment cluster 2 contained the highest number of downregulated protein The completeness to a set of single-copy protist orthologs was assessed using BUSCO, version 3. 0. 2  61. ). The size of each dot is proportional to the number of transcripts per cluster (indicated on the right). b Functional annotations retrieved with eggNOGmapper and significantly enriched in the given expression modules. The module median kinetics in the encystment condition are represented in the middle (upregulation and downregulation in the red and blue area, respectively). The detailed outputs of the enrichment can be found in Supplementary Data 2 file.
These were utilized to generate a custom proteomics database for protein identification by MS/MS. In this data set, we identified 8677 protein groups matched to sequences from the proteome of reference ("Uniprot"), the transcriptome ("trans. ") or present in both (gray). The protein groups quantified at both transcript and protein ("prot. ") level are presented on the right, next to the number of protein groups significantly regulated in the transcriptome (green), le proteome (purple) or both (black). b Normalized distribution of the log2-transformed fold changes relative to T0 control in the proteome (purple) and the corresponding transcripts (green) in the control condition (left) and during encystment (right). Time points are indicated next to the corresponding density. c Clusters of significantly regulated protein groups.
This number seems overestimated, as the reference genome and the recently published genome of Matthey-Doret et al. 38  only contain 14,974 and 15,497 genes, respectively. This high gene number could be the consequence of Trinity generation of isoforms and the presence of non-coding RNA. Sequence mapping can also lead to an overestimation of protein number due to protein isoforms and/or cleavages. However, our de novo approach was preferable as the BUSCO-completeness scores for the reference genome remain quite low. Based on the custom database for proteomic analysis our strategy allowed us to create using the newly built transcriptome, we identified 2701 protein groups absent from the reference proteome of A. castellanii, suggesting that the overestimation may not be as extreme. Our dataset provides a unique resource of protein sequences that may be specifically expressed during encystment.
RT-qPCR of encysting A. castellanii Primer design and efficiency. Specific primer pairs were designed to target transcripts identified in this study using Primer-BLAST (NCBI) (Supplementary Data 7). Sequences from our transcriptome were used with the following parameters: PCR product size from 70 to 200 bp, primer melting temperatures ranging from 57 to 63 °C with an optimal melting temperature at 60 °C and no exon/intron selection. Every primer pair were synthetized by Eurogentec (Belgium). Their efficiency was evaluated using A. castellanii genomic DNA. Briefly, a standard curve was constructed with qPCR results obtained with the designed primer pairs and a fivefold serial dilution of A. castellanii genomic DNA. The PCR amplification efficiency (E) was then determined for each primer pair using the slope of the standard curve.
Transcriptomic analysis A de novo transcriptome assembly. A total of 21 (7 conditions in triplicate) were sequenced at the GENEWIZ genomics service company (Leipzig, Germany), using the paired-end Illumina HiSeq libraries (2 × 150 bp reads). Preparation of the RNA library included a double rRNA depletion with both the Ribo-Zero rRNA removal kit (Human/Mouse/Rat) and the Ribo-Zero rRNA removal kit (Bacteria). All raw data are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database under the BioProject database identifier PRJNA794325. Data were processed using a FAIR workflow built specifically for this project. This pipeline and the assembled transcriptome are publicly accessible: https:// github. com/ifremer-bioinformatics/FLORA. Briefly, raw RNA-Seq read quality was inspected using FASTQC (https://www.
bioinformatics. babraham. ac. uk/projects/ fastqc/). An error correction step was performed using Rcorrector (version 1. 0. 3)  58 ith default parameters in order to correct sequencing errors in reads or to remove incorrect reads. To ensure to remove all rRNA contamination, all reads were mapped against the SILVA database (version 132)  59  using Bowtie2 (version 2. 2. 1)  60  with the '--very-sensitive' option. Only unmapped reads (corresponding to non-rRNA reads) were kept for further processes. Then, low-quality and short reads, sequencing adaptors, and "N" nucleotides were removed using TrimGalore (version 0. 6. 1) (https://www. bioinformatics. babraham. ac. uk/projects/trim_galore/). The Trinity package was used to assemble and annotate 1,075,406,542 reads (sum of paired reads) that represent ∼70% of the initial reads.