dataesr/scientific-paragraphs-categorization huggingface dataset:
ucsd. edu) and ProteomeXchange PXD020143 identifier (http://www. proteomexchange. org). automatic annotation. Therefore, we further filtered the list of our deregulated proteins by using the subcellular localisation of Uniprot annotation. Altogether, by considering GO terms and Uniprot annotations, twenty-nine secreted deregulated proteins were retained (Table  1  and Table  S3a ) to build a network of secreted deregulated proteins upon Mecp2 deficiency in the RTT astroglial secretome (Figure  1C ). Among these 29 secreted proteins, 11 were down-regulated while 18 proteins were up-regulated. The heat map of deregulated secreted proteins (Figure  1B ) confirmed the coherence of the iTRAQ ratio (Mecp2 KO vs WT) obtained from the two independent replicates with the highest fold change for Lcn2 (iTRAQ ratio KO/WT 0.
31), Lgals1 (iTRAQ ratio KO/WT FG 0. 35), Mfap4 (iTRAQ ratio KO/WT FG 2. 83), and Enpp5 (iTRAQ ratio KO/WT FG 2. 54) (Table  1 ). Although our analysis highlighted a small number of proteins for global profiling (29 deregulated secreted proteins), this study provides the first proteomic insight into astroglial-secreted factors in RTT. The secretomic data were deposited to the ProteomeXchange Consortium via the MassIVE Dataset Submission  [24]  under the MassIVE MSV000085665 identifier (http://massive. ucsd. edu) and ProteomeXchange PXD020143 identifier (http://www. proteomexchange. org). A green-black-red gradient was applied according to the iTRAQ ratio mean of down-regulated and up-regulated proteins. The circadian layout of the network is based on betweenness centrality. The diameters of nodes are proportional to betweenness centrality.
8) (Raw data Table  S4 ). Table  1. Refined list of 29 secreted proteins significantly dysregulated in glial secretome of mouse Rett syndrome (iTRAQ labelled quantitative proteomic data obtained independently from 2 independent replicates with, in each experiment, 3 pooled two-day-old male mice for each condition (Mecp2 KO and wild type)). Down-and up-regulated proteins are respectively in green and red in the table.
Validation of Proteomic Data by Western Blot Among the 29 deregulated proteins validated by mass-spectrometry analysis, we analyzed by Western blotting the four most relevant proteins on a translational level including both proteins (Lcn2 and Lgals3) with the highest betweenness centrality scores in the network of deregulated secreted proteins. We quantified protein level for Lcn2, Lgals3, B2M and Mfap4 (respectively -45%, -37%, -25%, and +55% of expression level by mass spectrometry in Mecp2 KO secretome compared to WT) in three new secretome samples of astroglial culture from WT and Mecp2 KO mice. Significant downregulation of Lcn2 (-42%), Lgals3 (-44%) and a trend of downregulation of B2M (-43. 4%) was confirmed in the Mecp2 KO astroglial secretome samples by Western blot (Figure  2  and Supplementary Figure  S1 ).
Although secretomic analysis showed an important upregulation of Mfap4, we only found a trend to an upregulation of Mfap4 (Figure  2 ). All Western blots were normalized with total protein staining (Figure  2 ). of astroglial culture from WT and Mecp2 KO mice. Significant downregulation of Lcn2 (-42%), Lgals3 (-44%) and a trend of downregulation of B2M (-43. 4%) was confirmed in the Mecp2 KO astroglial secretome samples by Western blot (Figure  2  and Supplementary Figure  S1 ). Although secretomic analysis showed an important upregulation of Mfap4, we only found a trend to an upregulation of Mfap4 (Figure  2 ). All Western blots were normalized with total protein staining (Figure  2 ). We also analyzed protein expression of these factors in astroglia culture. As Lcn2 is a secretory protein, its cellular levels were too low to be analyzed by Western blot (data not shown).
Lcn2 and Lgals3 Supplementation Improves Dendritic Arborization of Mecp2 KO Neurons An important feature of RTT is the reduced dendritic arborization that can be found in both RTT mouse models and RTT patients  [25, 26]  and was also shown in this study (Figure  3A ). Astrocytes are known to participate in neuronal dendritic growth  [27]. To correlate the observed deregulations in the astroglial secretome with RTT features, we tested the effect of two deregulated factors on neuronal dendritic morphology. We fo- We also analyzed protein expression of these factors in astroglia culture. As Lcn2 is a secretory protein, its cellular levels were too low to be analyzed by Western blot (data not shown). Interestingly, no significant differences in Lgals3, B2M, and Mfap4 protein levels were found between WT and Mecp2 KO astroglial cells (Supplementary Figure  S2 ), which suggests that the observed astroglial secretome deregulations could be the consequence of secretion rather than protein synthesis deficit.
GO Analysis Reveals Enriched Biological ProcessesiIn the RTT Astroglial Secretome Despite the above-mentioned limitations, we identified 557 proteins, including conventionally secreted and non-conventionally secreted proteins as well as cytosolic proteins. Hence, 105 proteins were significantly deregulated in the Mecp2 KO secretome compared to the WT one, in two independent biological replicates. Among these proteins, we focused our analysis on 29 proteins reported as secreted in the UniprotKB/Swissprot database. Although our analysis highlighted a small number of proteins for global profiling (29 deregulated secreted proteins), this study provides the first proteomic insight into astroglialsecreted factors in RTT. Further analysis based on GO terms revealed a neuro-inflammation signature in the RTT astroglial secretome with the up-regulation of proteins involved in the inflammatory response (C3, C4b, Hyal1) and down-regulation of proteins involved in eosinophil chemotaxis and cellular response to interleukin (Ccl2, Lgals3, Chil1, Lcn2).
Eluted fractions were automatically mixed with alpha-cyano-4-hydroxycinnamic acid matrix (HCCA, 2 mg/mL in 70% ACN and 0. 1% TFA) at a ratio 1:3 v:v and spotted on an Opti-TOF LC/MALDI Insert 123*81 mm plate (Applied Biosystems, Foster City, CA, USA) at a frequency of one spot per 15 s. Afterwards, spotted peptides were analyzed in MS and MS/MS modes using a 4800 MALDI-TOF/TOF mass spectrometer (Sciex, Les Ulis, France) operating in a positive reflector ion mode. The instrument was controlled by the 4000 Series Explorer software v. 3. 5. 3. Peptide Calibration Standard II (Bruker Daltonics, Bremen, Germany) was used to perform the external calibration with a mass tolerance of 50 ppm. MS spectra were acquired for each spot from 700 to 3500 m/z. The 30 most intense ions in each MS spectrum above a S/N (Signal/Noise) threshold of 30 were selected for MS/MS analysis.
iTRAQ Data Analysis MS and MS/MS spectra were used for identification and relative quantification using the ProteinPilot software v 4. 0 (Sciex, Les Ulis France) and the Paragon TM algorithm. The analysis was performed with the Mus musculus UniProtKB/Swiss-Prot database (European Bioinformatics Institute, Hinxton, UK). The Paragon searches were run using thorough ID and a false discovery rate (FDR) of 1%. Only proteins with at least one peptide above 95% confidence level were included for the quantification analysis. As described previously  [23, 68, 69] , the statistical analysis was performed by implementing the area of the iTRAQ reporter ions for each of these validated peptides into the R Package Isobar v. 1. 14. 0. [70]. A protein was considered as significantly deregulated when pValueRatio was obtained lower than 0.
The membrane was blocked with blocking buffer (Millipore, WBAVDFL01) for 1 h at room temperature. Primary antibodies for Lcn2 (1:1000, rabbit, abcam Ab63929), Lgals3 (1:1000, rabbit, abcam Ab76245), B2M (1:500, rabbit, Santa Cruz SC8362), Mfap4 (1:1000, rabbit, abcam ab169757), tubulin (1:5000, mouse, Sigma) and Actin (1:10000, mouse, Millipore), were diluted in the same solution and incubated overnight at 4 • C. The membrane was incubated using appropriate fluorescent secondary antibodies (IRDye 800 CW, IRDye 680 RD, LI-COR and LI-COR odyssey Imager). Quantitative analyses of signal intensity were performed using ImageJ software. For quantification in secretome samples, revert total protein stain (LI-COR Biotechnology, Bad Homburg, Germany) was used to normalize total protein quantity following the manufacturer's recommendations.
Horizontal lines indicate 95% confidence intervals. (D) Funnel plot summarizing the evolution of protein-coding genes specifically expressed in different tissues of the human body (Supplemental Table  S3 ). involved in nervous system development (OR = 1. 2, P = 2. 4 × 10 -9 ) and synaptic transmission (OR = 1. 35, P = 1. 7 × 10 -8 ). We investigated the possible enrichment of specific tissues in PSGs and SCGs by analyzing RNA-seq (Illumina Bodymap2 and GTEx), microarray, and proteomics data sets (Methods). For expression data, despite that virtually no gene is expressed only in one tissue, we calculated a tissue specificity score for each gene by normalizing their profile across tissues (for more details, see Supplemental Fig. S3 ). The results confirmed a higher degree of conservation for protein-coding genes more specifically expressed in the brain (Wilcoxon rank correlation rc = -0.
1, P = 4. 1 × 10 -12 , bootstrap-corrected for gene length and GC content) than for those expressed elsewhere in the body, with the greatest divergence observed for genes expressed in the testis (Wilcoxon rc = 0. 3, P = 7. 8 × 10 -11 , bootstrap-corrected for gene length and GC content) (Fig. 1D ; for a replication with GTEx data, see also Supplemental Table  S3 ; Supplemental Fig. S4 ). This conservation of brain protein-coding genes was replicated with two other data sets (MicroArray: Wilcoxon OR = -0. 18, P = 1. 8 × 10 -12 ; mass spec-trometry: Wilcoxon rc = -0. 21, P = 1. 55 × 10 -9 ; bootstrap-corrected for gene length and GC content).
ucsc. edu/downloads. html; date of access online August 13, 2015). All the PSGs had their protein sequence deduced from our analysis compared manually to the one in the protein database. All variants matched, and we did not find any alignment artifact. The core annotations used for our study were not available for the GRCh38 version of the human genome when we started this project. Because one of the biggest improvements in GRCh38 is the annotation of the centromere regions  (Guo et al. 2017) , a switch from GRCh37 to GRCh38 would not affect our conclusions. Moreover, regarding the coding regions of the human genome, the number of nonsynonymous detected by GRCh38 (N = 22,796 SNVs) is very similar to GRCh37's (N = 22,622 SNVs)  (Guo et al. 2017 , Table  3 ).
VCF annotation We combined the VCF file from  Castellano et al. (2014)  with the VCF files generated from the ancestor and primate sequence alignments. The global VCF was annotated with ANNOVAR (version of June 2015)  (Wang et al. 2010) , using the following databases: refGene, cytoBand, genomicSuperDups, esp6500si-v2_all, 1000g2014oct_all, 1000g2014oct_afr, 1000g2014oct_eas, 1000g2014oct_eur, avsnp142, ljb26_all, gerp++elem, popfreq_ max, exac03_all, exac03_afr, exac03_amr, exac03_eas, exac03_fin, exac03_nfe, exac03_oth, exac03_sas. We also used the ClinVar database (https://ncbi. nlm. nih. gov/clinvar/; date of access February 3, 2016).
ω GC12 calculation Once all the alignments had been collected, we extracted the consensus coding sequences (CCDS) of all protein-coding genes referenced in Ensembl BioMart GRCh37, according to the HGNC (date of access May 5, 2015) and NCBI Consensus CDS protein set (date of access August 10, 2015). We calculated the number of nonsynonymous mutations N, the number of synonymous mutations S, the ratio of the number of nonsynonymous mutations per nonsynonymous site d N , the number of synonymous mutations per synonymous site d S , and their ratio d N /d S (also called ω) between all taxa and the ancestor, using the yn00 algorithm implemented in PamL software  (Yang 2007). We avoided infinite and null results by calculating a corrected version of d N /d S. If S was null, we set its value to 1 to avoid having zero as the numerator.
Gene sets We used different gene sets, starting at the tissue level and then focusing on the brain and key pathways. For body tissues, we used Illumina Body Map 2. 0 RNA-seq data, corresponding to 16 human tissue types: adrenal, adipose, brain, breast, colon, heart, kidney, liver, lung, lymph, ovary, prostate, skeletal muscle, testes, thyroid, and white blood cells (for more information: https://www. ebi. ac. uk/gxa/experiments/E-MTAB-513; data preprocessed with Cufflinks, accessed May 5, 2015, at http://cureffi. org). We also used the microarray data set of  Su et al. (2004)  (Human U133A/ GNF1H Gene Atlas, accessed May 4, 2015, at http://biogps. org). Finally, we also replicated our results with recent RNA-seq data from The GTEx Consortium (2015) (https://www. gtexportal. org/ home/). For the brain, we used the data set of  Su et al.
NeuroVault analyses We used the NeuroVault website  (Gorgolewski et al. 2015)  to collect 19,244 brain imaging results from fMRI-BOLD studies (T and Z-score maps) and their correlation with the gene expression data  (Gorgolewski et al. 2014)  of the Allen Brain Atlas  (Hawrylycz et al. 2012 ). The gene expression data of the Allen Brain Atlas were normalized and projected into the MNI152 stereotactic space used by NeuroVault, using the spatial coordinates provided by the Allen Brain Institute. An inverse relationship between cortical and subcortical expression dominated the pattern of expression for many genes. We thus calculated the correlations for the cortex and subcortical structures separately.
Allen brain data We downloaded the Allen Brain Atlas microarray-based gene data and multiple cortical areas: Smart-seq from the Allen Brain website (accessed on July, 2020, at http://www. brain-map. org). Microarray data were available for six adult brains; the right hemisphere was missing for three donors, so we considered only the left hemisphere for our analyses. For each donor, we averaged probes targeting the same gene and falling in the same brain area. We then subjected the data to log normalization and calculated Z-scores: across the 20,787 genes for each brain region to obtain expression levels; and across the 212 brain areas for each gene to obtain expression specificity. For genes with more than one probe, we averaged the normalized values over all probes available. The Smart-seq data set followed a similar preprocessing and led to expression level and specificity of 32,165 genes across 363 cell types.
Acknowledgments We thank  J. -P. Changeux ,  L. Quintana-Murci ,  E. Patin ,  G. Laval ,  B. Arcangioli ,  D. DiGregorio ,  L. Bally-Cuif ,  A. Chedotal ,  C. Berthelot ,  H. Roest Crollius , and  V. Warrier  for advice and comments, and we thank the members of the  Human Genetics and Cognitive Functions laboratory  for helpful discussions. We also thank  C. Gorgolewski ,  R. Carter ,  M. Haeussler ,  M. Verhage , and the  SynGO consortium  for providing key data sets without which this work would not have been possible. This work was supported by the  Institut Pasteur ;  Centre National de la Recherche Scientifique ;  Paris Diderot University ; the  Fondation pour la Recherche Médicale  ( DBI20141231310 ); the  Human Brain  Project; the  Cognacq-Jay Foundation ; the  Bettencourt-Schueller Foundation ; the  Agence Nationale de la Recherche (ANR)  (SynPathy); and the  Innovative Medicines Initiative 2  [No.
777394 ]. This research was also supported by the  Laboratory of Excellence GENMED (Medical Genomics)  Grant No. ANR-10-LABX-0013 ,  Bio-Psy , and by the  INCEPTION program   ANR-16-CONV-0005 , all managed by the  ANR  part of the Investments Evolution of brain protein-coding genes in humans Genome Research 493 www. genome. orgCold Spring Harbor Laboratory Press on August 25, 2021 -Published by genome. cshlp. orgDownloaded from for the  Future program. G. D. is funded by the  Institute for Data Valorization (IVADO) ,  Montreal , and the  Fonds de Recherche du Québec (FRQ ,  295291 ;  295289 ). The funders had no role in study design, data collection, and analysis, the decision to publish, or preparation of the manuscript.
Solved crystal structures for members of the distantly related CpcS/U (Protein Data Bank, (PDB): 3BDR;  (4, (15) (16) (17) (18) ) and CpcT (PDB: 4O4O;  (19, 20) ) lyase families show that they adopt a similar antiparallel beta-barrel structure. CpcS-type lyases are hypothesized to have evolved first because they recognize the central C82-equivalent position present in α and β subunits of allophycocyanin, β-phycocyanin, and β-PE  (4, 10, 11, 15, (19) (20) (21) (22). Unrelated to the other clans, the CpcE/F lyase clan members display a high specificity for a single bilin and a single binding site on a particular PBP (PDB 5N3U;  (13, 14, 23, 24) ). These lyases contain five to six HEAT-repeat motifs (thought to facilitate protein-protein interactions) coupled with Armadillo repeats (25-28) (Fig.
Structural prediction The putative lyase MpeV of the marine Synechococcus strain RS9916 shares sequence similarity (54. 9%) with the recently characterized CpeF lyase from the freshwater cyanobacterium F. diplosiphon, and a structural prediction analysis using Phyre 2 shows that they have similar predicted structures (Fig. S1 ; closest characterized structure PDB: bgfp-a)  (48) , suggesting that these enzymes may have a similar substrate specificity for β-PE subunits. Yet, RS9916 has PUB doubly linked at C50, 61 of CpeB and MpeB  (31) , rather than PEB at the equivalent C48, 59 positions of F. diplosiphon CpeB  (46). This led us to hypothesize that MpeV could be a lyase isomerase acting at C50, 61 on CpeB and perhaps also on MpeB. Of note, in some marine strains of Synechococcus, the β-subunits contain PEB in place of PUB at C50, 61 on CpeB, similar to F.
Phylogenetic comparisons A Bayesian tree was generated for the MpeU, MpeV, MpeX, and CpeF protein families. MpeU, MpeV, MpeX, and CpeF protein sequences were retrieved either from Genbank or from the Cyanorak v2. 1 database (www. sb-roscoff/cyanorak) for Prochlorococcus and Synechococcus genomes (Fig. 3  and Table  S4 ). Phylogenetic reconstructions were performed using Maximum Likelihood (ML; PhyML v3. 0) and Bayesian Inference (BI; MrBayes v3. 1. 2). Likelihood scores of 120 potential evolutionary models were evaluated using the Akaike Information Criteria (AIC)  (55)  and Bayesian Information criteria (BIC)  (56) , as implemented in Protest 3. 4. 1  (57). ML reconstructions were performed using PhyML (v3. 0)  (58)  with the Le and Gascuel substitution model, with estimation of the Γ distribution parameter, fraction of invariant sites, and character frequencies (LG+I + F + G model) (59) and using 100 bootstrap replicates.
Bayesian inference was done by invoking the appropriate "nst" and "rates" settings in the software package MrBayes 3. 1. 2 (v3. 2. 1) (60) using the same  model. The Markov Chain Monte Carlo (MCMC) analysis algorithm was used to estimate the posterior probability distribution for each collection of sequences using one incrementally "heated" chain with three "cold" chains, these four chains being replicated four times per analysis, a random starting tree, and sampling every 100th generation. Bayesian posterior probabilities were generated from 5,000,000 generations, and the first 12,500 generations were removed as burnin. All reconstructions were visualized using Archaeopteryx Version 0. 9901  (61) , and the tree was drawn using iTOL  (62).
Anti-CpeB antibodies were generated against holo-CpeB purified from F. diplosiphon  (15). Membranes were further washed and incubated with secondary antibodies as previously described  (46). Luminal/enhancer and peroxide reagents (Bio-Rad, Hercules, CA) for enhanced chemiluminescence were mixed in a 1:1 ratio and incubated with the membrane for 1 min. Chemiluminescence was detected by a Chemi-Doc MP Imaging System (Bio-Rad, Hercules, CA). Membranes were stored in TBST at 4 C. Protein volume intensities were quantified and analyzed using Image Lab Software V5. 2. 1 (Bio-Rad, Hercules, CA).
Samples were separated by an Agilent 1100 CapLC with a 0. 3 × 100 mm Zorbax SB300 C18 column using conditions described in Kronfel et al. (34). All data processing was performed with Thermo XCalibur 4. 0, Proteome Discover 2. 1. 1. 21 (Thermo), and a local copy of ProteinPropector 5. 22. 1 (prospector. ucsf. edu) (64). The FASTA file searched had the seven recombinant protein sequences (Table  S1 ), and the following parameters were used to generate the in silico peptide library. Trypsin digestion, SEQUEST search engine, 10 ppm precursor tolerance, 0. 02 Da fragment mass tolerance, two missed cleavage sites per peptide, a, b, and y ions considered, up to four modifications were allowed to a single peptide including oxidized methionine (+O), mercaptoethanol addition to Cys (+C 2 H 4 OS), bilin addition to Cys (+C 33 H 38 N 2 O 6 ), oxidized bilin addition to Cys (+C 33 H 38 N 2 O 7 ), and deamidation of Gln or Asn (-NH, +O), and loss of NH 3 from peptide N-terminal Asn residues, loss of protein N-terminal Met, or acetylation of N-terminus.
1 M NaP pH 7, 0. 5 M KFe 3 [CN 6 ], 0. 5 M KFe 4 [CN 6 ], 1 mg/ml X-Gluc). Then, 150 µl fresh GUS Buffer was added to each well and the plates incubated at 37 °C for 24 h. An image of the 24-well plates was captured every 2 h for 10 h using a scanner. A final image was captured after 24 h of incubation. Quantification of the β-glucuronidase activity was made by converting the color intensity into a grayscale, from zero (black) to 255 (white), using ImageJ software. The T 0 and T 6 photos were uploaded onto the software and converted into 8-bit pictures. An identical scale of 40. 4 pixels/mm was defined to obtain homogeneous data. A circular selection covering the interior surface of a well was used to measure 1) the area, 2) minimum grey level, 3) maximum grey level, and 4) mean grey level.
The size of fragmented DNA was checked by Bioanalyzer with the DNA12000 kit (Agilent Technologies). DNA fragments were between 8 and 10 kb. Whole genome sequencing was carried out by MinION (Oxford Nanopore Technologies). MinION sequencing libraries were prepared according to the 1D Genomic DNA by Ligation (SQK-LSK109) protocol supplied by the MinION manufacturer (Oxford Nanopore Technologies). Five samples were barcoded and sequencing 24 h on the FlowCell. The raw data in fast5 format were generated by the MinKNOW software (version 3. 6. 5). Then, demultiplexing and base calling were performed using Guppy software (version 3. 6. 0) to obtain a sequence file in fastq format for each sample. A quality control of the data was then performed using Nanoplot and fastqc. Then, flye (version 2. 8.
The sequencing of these five clones showed large deletions in the uidA sequence (data not shown). These clones were NAT-resistant, as the NAT gene was full length, without β-glucuronidase activity due to truncation of the uidA gene. Intriguingly, CLOSITRA 36 and 40 did not carry any transgene sequence modifications to explain their GUS (-) phenotype. We wished to determine whether clones 36 and 40 show no activity or weak non-detectable activity. We thus developed a fluorometric assay based on the cleavage of 4-methylumbelliferylβ-D-glucuronide (MUG), a fluorogenic substrate, by β-glucuronidase  (Mead et al. , 1955). Upon cleavage, the fluorescent moiety 4methylumbelliferyl (4-MU) is released and detected by excitation (approximately 360 nm). Ten-hour kinetic monitoring of the fluorescence in GUS (+) CLOSITRA protein extracts was performed.