dataesr/scientific-paragraphs-categorization huggingface dataset:
Cytosolic and mitochondrial acetyl-CoA are required for extensive acetylation of glycolytic and TCA cycle enzymes, respectively To better understand the consequences of the differential acetylation in ΔACL/iΔACS and ΔBCKDH parasites, we aimed to identify the affected biological processes by performing Gene Ontology (GO) enrichment using the R-package topGO  [29]. Comparing the differentially acetylated proteins to the entire genome/proteome of T. gondii, we established that the affected proteins cluster into 12 significantly enriched (p < 0. 001) biological processes including histone acetylation (33-fold enrichment), chromatin organisation (9-fold enrichment), protein acetylation (19-fold enrichment) and carbohydrate metabolic processes (4-fold enrichment) (Fig. 5a ). To avoid bias towards proteins which were identified as acetylated in this study, we performed the GO enrichment analysis of differentially acetylated proteins against the relatively small subset of 269 acetylated proteins as background (Additional file 5: Figure  S4e ).
Nevertheless, hypo-acetylation of these secretory proteins in ΔACL/iΔACS parasites provides the first evidence that their acetylation relies on the cytosolic acetyl-CoA pool. Next, we analysed the affected metabolic pathways by comparing the differentially acetylated proteins against the entire genome/proteome of T. gondii and using the metabolic pathway enrichment tool on ToxoDB (https:// toxodb. org/) based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database as the source. The highest metabolic pathway enrichment was found for proteins functioning in glycolysis/gluconeogenesis (9-fold). Most glycolytic/gluconeogenic enzymes were hypo-acetylated in ΔACL/iΔACS parasites (Fig. 5b ). Although enzymes implicated in glycolysis are extensively acetylated in other organisms  [37] [38] [39] , the effects that acetylation has on enzyme activity varies and depends on the organism, enzyme, site and context  [37] [38] [39] [40] [41].
6  (See legend on next page. ) acetylated in ΔBCKDH parasites. Differentially acetylated proteins in ΔBCKDH parasites were compared to the total genome/proteome using the R-package topGO  [29] , which identified affected proteins to be significantly enriched (p < 0. 001) in 10 biological processes, most of which are related to the TCA cycle and respiration, including the top hits with over 30-fold enrichment (tricarboxylic acid metabolic processes, tricarboxylic acid cycle and citrate metabolic processes) (Fig. 6a ). In order to avoid bias towards acetylated proteins, differentially acetylated proteins were probed against the background of the small subset of acetylated proteins. Using this approach, 6 biological processes were identified as significantly enriched (p < 0. 05) all of which related to the TCA cycle and respiration and 5 of which were also identified in the enrichment analysis against the entire genome/proteome (Additional file 5: Figure  S4f ).
As expected, analysis of proteins which were differentially acetylated in ΔBCKDH parasites revealed major enrichment in the TCA cycle when using the metabolic pathway enrichment tool on ToxoDB (https://toxodb. org/) based on the KEGG database as the source, returned the TCA cycle as top-hit (24-fold enrichment). Seven out of eight TCA cycle enzymes were hypo-acetylated in ΔBCKDH parasites compared to RH cells (Fig. 6b ). As for glycolytic enzymes, the role of TCA cycle enzyme acetylation varies and has not been defined for T. gondii enzymes  [41, 43]. One of the most significantly hypo-acetylated proteins in ΔBCKDH cells was lactate dehydrogenase (LDH1) (Additional file 7: Table  S3 ). Acetylation in lysine K5 has been reported to negatively regulate LDH activity in mammalian cells  [44]. Although we observed hypo-acetylation of a different lysine (K218) in ΔBCKDH parasites, it may similarly increase LDH activity, as lactate production is markedly increased in ΔBCKDH parasites  [13].
Lack of ACL/ACS or BCKDH alters the transcriptome and proteome of T. gondii For a global assessment of the impact caused by the depletion of cytosolic acetyl-CoA on gene expression in ΔACL/ iΔACS parasites, we performed a combination of expression profiling by ribonucleic acid sequencing (RNA-seq) and quantitative proteomics analyses on freshly egressed extracellular tachyzoites. The complete datasets are available in data repositories  [28, 48]. We focused our analysis on transcripts and proteins that were changed ≥ 2-fold in their level (p value < 0. 01) in ΔACL/iΔACS parasites compared to RH. RNA-seq analysis allowed the identification of 453 differentially regulated transcripts out of 7250; 377 were found to be upregulated while 76 were downregulated (Fig. 7a , Additional file 9: Table  S4 ).
7b, c ). This relatively small overlap between the datasets highlights the diverse and complex consequences of cytosolic acetyl-CoA depletion. Similarly, expression profiling by RNA-seq and quantitative proteomics was performed on freshly egressed ΔBCKDH tachyzoites to obtain a more holistic assessment of the consequences of mitochondrial acetyl-CoA depletion. Transcriptomic analysis revealed that a   highlighting  genes which were found to be significantly up-(red background) and downregulated (blue background) at the transcriptome and proteome level in ΔBCKDH parasites. BCKDH, branched-chain α-keto acid dehydrogenase-complex; ACL, ATP citrate lyase; ACS, acetyl-CoA synthetase; RNA, ribonucleic acid relatively small subset of transcripts (175 out of 6926) was differentially expressed (≥ 2-fold change, p value < 0.
05) in carbohydrate metabolic processes and cellular protein modification processes, with 3 and 4 proteins affected, respectively (Additional file 12: Table  S7 ). Comparison of the different datasets revealed no overlap of the altered acetylome with either the transcriptome or proteome. However, 8 genes were differentially expressed simultaneously at the transcript and protein level (Fig. 7e, f ), 3 of which were significantly upregulated at the transcript and protein level, while 5 were downregulated at both levels, including 3 rhoptry proteins, which are secreted during parasite invasion  [36]  (Fig. 7f ). One gene, for which expression was upregulated over 2fold at the transcript and protein levels in ΔBCKDH parasites, belonged to the formate/nitrite transporter (FNT) family (TGME49_209800, FNT-1) (Fig.
RNA sequencing and data analysis Isolated RNA was subjected to 100-bp single read sequencing on an Illumina HiSeq 2500 at the iGE3 genomics platform of the University of Geneva (http://www. ige3. unige. ch/genomics-platform. php) (Genomics platform, Institute of Genetics and Genomics iGE3, University of Geneva). Two sets of samples (2 × 3 replicates) were loaded per sequencing lanes of the flow cell. Adapter sequences from the raw reads were trimmed using FASTX-Toolkit (http://hannonlab. cshl. edu/fastx_ toolkit/) (phred<20). Following quality control, resulting reads were aligned to the T. gondii reference genome (ToxoDB-12) with TopHat/Bowtie2 aligner, and HTSeqcount was used to generate the read counts of the genes  [71] [72] [73] [74]. edgeR, a Bioconductor package in R (http:// www. R-project.
Analytical triplicates were acquired for the comparison of acetylomes between ΔBCKDH mutant strain and its parental RH strain. For total proteome analyses, survey full-scan MS spectra (m/z 400-1600) were acquired with a resolution of 70,000, with AGC target set to 10 6 ions (maximum filling time 200 ms) and with lock mass option activated. The 10 most intense ions were fragmented by higherenergy collisional dissociation (nce = 30) with a resolution of 17,500, with AGC target set to 10 5 ions (maximum filling time 50 ms). MS and tandem MS data were acquired using the Xcalibur software (Thermo Scientific). Analytical quadruplicates were acquired for comparison of total proteomes of ΔBCKDH mutant strain and its parental RH strain. Raw and processed MS data have been deposited in a data repository  [28, 75].
Peptide, protein and acetylated site identifications RAW files were processed using MaxQuant  [76]  version 1. 5. 8. 3. Spectra were searched against the T. gondii database (ME49 taxonomy, version 30 downloaded from ToxoDB  [77] , the Swiss-Prot database (Homo sapiens taxonomy, 2017-08-24 version), the frequently observed contaminants database embedded in MaxQuant and the corresponding reverse databases. Trypsin was chosen as the enzyme, and two or three missed cleavages were allowed for respectively total proteome and acetylome analyses. Precursor and fragment mass error tolerances were set at their default values. Peptide modifications allowed during the search were carbamidomethyl (C, fixed), acetyl (protein N-term, variable), oxidation (M, variable) for total proteome analyses, and acetyl (K, variable) was added for acetylome analyses.
Statistical analyses Statistical analyses were performed using ProStaR  [78]. Peptides and proteins identified in the reverse and contaminant databases or matching to human sequences were discarded. For the comparison of acetylomes between ΔBCKDH mutant strain and its parental RH strain, intensities of the acetylated sites were deduced through averaging the values extracted from the analytical triplicates. For differential analysis of acetylomes, acetylated sites were split into several lanes when they were identified on peptides bearing various numbers of acetylated sites to allow the quantification of the different peptidoforms. Only proteins and acetylated sites detected in at least 3 replicates of one condition were conserved. After log2 transformation, protein LFQ intensities and acetylated site intensities were median normalised before missing value imputation (replacing missing values by the first percentile value of each column).
GO enrichment analyses GO enrichment analyses of transcripts and proteins was carried out using the tool for enrichment in biological processes on ToxoDB (https://toxodb. org/) against the total T. gondii database with a p value cut-off of 0. 05 (limited to GO slim terms function). GO enrichment analysis of the acetylome was performed using the R package, topGO (version 2. 38. 1)  [29]. Enrichment factors were calculated as the ratio of the significant genes assigned to the GO to the expected number of assigned genes defined by topGO. Enrichments in biological processes were determined in comparison with either the total T. gondii genome/proteome (p value cut-off, 0. 001) or against the subset of identified acetylated proteins (p value cut-off, 0. 05).
Over 850 metabolites were putatively identified based on accurate mass and standard retention time (where available) (confidence score 1 in Additional file 2: Table  S1 ), or predicted retention time (confidence score 2 in Additional file 2: Table  S1 )  [81]. Metabolite abundance was determined by LC-MS peak height and was normalised to the average for wild-type (RH) samples. Statistical analyses utilised Welch's t-test (α = 0. 05), and pathway analysis used KEGG annotations (Microsoft Excel). Stable isotope labelling was analysed using mzMatch-ISO  [82]  to extract all isotopologues of all putatively identified metabolites based on retention time and accurate mass. The complete dataset is available in a data repository  [24].
FA profiling by GC-MS RH and ΔACL/iΔACS intracellular parasites were incubated in the absence of Shld-1 for 16 h and simultaneously labelled with either 10 mM U-13 C-glucose or 2 mM U-13 C-acetate (Cambridge Isotope Laboratories). Per sample, 10 8 parasites were isolated and purified as described above. FAs were extracted, derivatised and analysed as described previously  [16]. Fatty acid methyl esters (FAMEs) were identified based on retention times and the library integrated in Xcalibur (Thermo Fisher Scientific). The abundance was determined based on the peak intensity relative to the cholesterol signal intensity in each sample. Abundance of the C14:0 and C26:1-FAME mass isotopologues (m/z 242-256; m/z 376-390) was determined using Xcalibur (Thermo Fisher Scientific) and OpenChrom software.
Polar metabolite profiling in PEPCK-1 acetylation mimetics Intracellular parasites were cultured in the presence of equimolar (8 mM) levels of natural-abundance glucose and U-13 C-glutamine (Cambridge Isotope Laboratories) for 24 h. Freshly egressing parasites (10 8 cells) were purified and harvested and metabolites extracted as described above. Polar metabolite extracts were prepared and analysed as described previously  [16]. Aspartate and glucose-6-phosphate were identified based on their retention time and ion spectrum of authentic standards. The signal for mass isotopologues of major ions of aspartate (m/z 232-235) and glucose-6-P (m/z 471-475) was determined using Xcalibur (Thermo Fisher Scientific) and OpenChrom software. The extent of 13 C-labelling in these ions was determined using the Excel (Microsoft) software following correction for the occurrence of natural isotopes as described by Zamboni et al.
The dried medium samples and cell extracts were derivatised using methoxyamine hydrochloride (Sigma-Aldrich/Merck) and N,O-bis(trimethylsilyl)trifluoroacetamide trimethylchlorosilane (Sigma-Aldrich/ Merck) as described previously  [16]. Metabolites were identified based on the retention time and ion spectrum of authentic standards and were quantified by integrating the area under the peak in the total ion chromatogram (TIC) using Xcalibur (Thermo Fisher Scientific) and OpenChrom software and comparing their abundance to that of the internal standard scyllo-inositol (intracellular metabolites) or to the abundance of valine (medium samples). The relative abundance of metabolites was determined using the Excel (Microsoft) software. Displayed data represent the average of 3-5 biological replicates.
We also test stability with respect to the simulation database used in point 5 of the list shown in Sect. 2. 2. 3. We split the sets of 300 signal and noise simulations into two halves, and build two independent likelihoods from each of them. When independently estimating τ with each of them, we find that the posteriors closely overlap (see Fig. 11 ). together with the 68 % (red) and 95 % (yellow) confidence levels for τ, obtained using either the complete set of 300 FFP10 simulations or the first half or the second half of them. The vertical line shows the best-fit value from the default analysis, i. e. , using 300 simulations.
The degeneracy with foreground parameters is clear in Fig. 82 , where we show a comparison between the coadded TT data when assuming either the ΛCDM foreground parameters or the A L ones. As discussed in Planck Collaboration  Int. LI (2017) , the A L deviation is somewhat dependent on the low power in the multipoles below ≈ 30. Cutting those multipoles yields slightly lower values of A L = 1. 21 ± 0. 10. Also, the A L deviation is related to the difference between the ΛCDM parameters measured from TT at < 800 and > 800, as already discussed in the previous subsection. We also check that the A L deviation cannot be explained by a mistreatment of the aberration due to the motion of the Solar System with respect to the CMB frame, which, as discussed in the following section, might also create an oscillatory residual in the power spectrum (but of too small an amplitude).
We thus conclude that an error in the transfer function cannot account for the A L deviation. In conclusion, the A L deviation is a feature of the Planck data mainly driven by the T T power spectrum. The polarization data cannot provide a robust test of whether this has a physical origin, due to the degeneracy with the PE corrections. A systematics explanation of this feature would require us to introduce a common mode to different frequencies and hence detectors in the focal plane, which is not trivial to achieve. At present, we have not found any conclusive evidence that this deviation is due to a systematic effect. A physical explanation of this deviation is also not easy to find, since many extensions of the ΛCDM model that we tested, which can provide effects similar to A L , are constrained by the addition of external data sets (and in particular the lensing reconstruction).
As discussed in PPL15, this bias was reduced when including the TE and EE data in the joint likelihood. While these results on FFP8 were found to be very reassuring, those simulations only incorporated a simplified model of the noise and systematic residuals. This limitation was overcome by the FFP10 simulations, which have a higher fidelity modelling of noise and systematics. In PPL15 (Sect. 3. 7), only the first few FFP10 simulations available at the time were analysed. In this work, we expand this analysis to the 300 FFP10 simulations, which were also used to build and validate the HFI-based low-polarization likelihood presented Sect. 2. 2. The FFP10 simulations are described in great detail in HFI18, and we repeat here only their most important features. Ideally, in order to build a simulation, one should generate a sky realization (CMB plus foregrounds in different frequency bands), apply the instrumental beam, and produce synthetic TOIs using the Planck scanning strategy.
Those TOIs then need to be convolved with the instrumental effects, which include noise and systematics, before applying the Planck dataprocessing and mapmaking pipeline. All these steps are numerically very costly, and to simplify them it was decided to divide the process in two parts. Firstly, 1000 sky realizations (including convolution by the beams and scanning strategy at the map level) are produced. Secondly, 300 end-to-end (E2E) simulations of the HFI data are made, implementing the full pipeline de- Upward shifts of up to 1. 5 σ in foregrounds are associated with downward shifts in cosmological parameters of close to 1 σ and are due to chance correlations in the particular CMB and foreground realization that we are using for the simulations. This accounts for the shifts in foreground amplitudes found in Fig.
For the same reason we will first compare the cosmological parameters recovered from the E2E simulations to the ones obtained on the input CMB maps (i. e. , the CMB before applying beam and scanning strategy effects) and to the ones obtained at different levels of complexity of the simulations, which will separately explore the chance correlations with foregrounds and with beams and scanning-strategy effects. We already validated most of the likelihood using the FFP8 simulations in PPL15, so we will only focus here on the effect of the higher fidelity modelling of noise and systematics. Similarly, we will separately present the TT, TE, and EE results, while we will not discuss joint results that have already been validated using FFP8. We adapt the likelihood approximation we used for the data to the E2E specific case.
Power spectra are still computed on the simulated half-mission E2E maps using the same masks as used for the data (even though the E2E simulations do not include point sources). The power spectra are beam-corrected using the same window functions used in the data (including the convolution by the scanning strategy), since the E2E simulations are designed to have the same effective beams. 25 The noise evaluation, which is needed to build the covariance matrices, is performed differently than for the data, since the HR maps are not available in the simulations (see Sect. 3. 3. 3) ; we measure the noise directly from the variance of the E2E simulations (since we have the same sky realization in each). Moreover, we simplify the foreground model using the approach we validated for the analysis of the FFP8 simulations in 2015.
Joint likelihood As in the previous releases, the final joint Planck CMB likelihood is formed using a combination of the different approx-   imations described in the earlier parts of this paper. This hybrid low-/highapproach has been advocated in  Efstathiou (2004)  and  Efstathiou (2006)  and applied to WMAP data (first in  Spergel et al. 2007) , as well as being used for previous Planck releases (PPL13; PPL15). Given the numerous different likelihood approximations described in this paper, we can build different hybridization schemes, depending on specific goals, be it obtaining better constraints on cosmological parameters, exploring all of the available modes in the data, or marginalizing over the numerous nuisance parameters introduced in the high-likelihood to speed up The baseline hybrid likelihood that is used in most of the 2018-release parameters paper (PCP18), labelled Planck TT,TE,EE+lowE, consists of a combination of the Commander likelihood, the SimAll likelihood, and the Plik likelihood.
Summary and Conclusions The Planck 2018 power spectra are displayed in Figs. 57, 58, and 59. These CMB power spectra, along with the likelihood approximations we described in this paper and the lensing likelihood described in (Planck Collaboration VIII 2020), form the basis of the main cosmological results of Planck, as presented in PCP18 and Planck Collaboration X (2020), and summarized in Planck Collaboration I (2020). The tightening and improved robustness of the cosmological constraints thanks to the full use of the Planck polarized data are the main achievements of the 2018 release and the main topic of this paper. The Planck reference likelihood approximation used in this release is very similar to the approach taken in the previous releases in 2013 (PPL13) and 2015 (PPL15). Different approaches are used for the low-multipole ( < 30) and highmultipole ( ≥ 30) regimes.
2. 2. The new likelihood at large scales provides the tightest cosmological constraint achieved to date on the reionization optical depth parameter, τ. This 2018 approach at large scales differs from the 2013 approximation (PPL13), which used the same Commander method for temperature and a pixel-based approximation using WMAP data for polarization, along with Planck HFI 353-GHz polarization data to mitigate dust contamination. It is also different from the 2015 method (PPL15), which used a single pixel-based approximation for both temperature and polarization, adopting a combination of the Commander best CMB temperature map and the LFI 70-GHz polarization maps (corrected for dust and synchrotron contamination using, respectively, the HFI 353-GHz and LFI 30-GHz data). The 2015 pixel-based approach allowed us to probe the correlation between temperature and polarization at large scales; this ability is important in order to discriminate between some extended cosmological models involving B-mode correlations, as well as non-rotationally invariant cosmologies, provided that the CMB signal-covariance matrix is appropriately modified.
In contrast, a power-spectrum-based likelihood, such as the 2018 baseline, may not be used directly to test isotropyviolating models, since rotational invariance is assumed in its derivation. For this reason, we also improved in this release the 2015 low-method and have described an updated version of it in Sect. 2. 3, which includes more data and a more thorough validation than in 2015. Owing to its lower S/N, this lowTEB likelihood is not as constraining as the baseline choice, but its results are entirely consistent with it, providing further validation. We remark that lowTEB is the only data set we provide that accounts for correlations between temperature and polarization at large scales. In the case of polarization, HFI18 describes the central role of the end-to-end simulations for modelling the largescale systematics that previously prevented the use of HFI data in this regime (an approach that was already outlined in Planck Collaboration Int.