dataesr/scientific-paragraphs-categorization huggingface dataset:
Data presented as box and whiskers plot. Means are indicated with a plus. Data were analyzed using two-tailed unpaired Student's t-test. (C,D) Induction of dnPKA specifically in blood vessel endothelial cells using Pdgfb-iCre led to a much slower and modest development of edema than in dnPKA iEC (C) and no hypoglycemia (D). Here, 10 control (Tg(Pdgfb-icre/ERT2) 1Frut without floxed transgenes; black) and 5 mutant (red) mice were analyzed (for (C)). For (D), 5 control (black) and 3 mutant (red) mice were analyzed. For panels (C,D), mice (males and females) were injected with tamoxifen at the age of 1-3 days weights, and blood glucose levels were measured starting with the day 40 after injection (40 dpi). dpi, days post injection. Data (C,D) were analyzed using two-way ANOVA followed by Sidak's multiple comparison test.
To study whether reduced production of albumin by the liver is responsible for hypoalbuminemia, we determined the hepatic expression of albumin by QPCR and Western blotting. While the level of albumin mRNA was increased (Figure  8A ), the albumin protein level in the liver of dnPKA iEC mice was unchanged compared to the control mice (Figure  8B, D ). Thus, hypoalbuminemia is not a consequence of reduced albumin production by the liver. Rather, drainage of the albumin into the subcutaneous space, as indicated by albumin-binding tracer Evans blue, seems to be the cause for hypoalbuminemia. Taken together, these data demonstrate that hepatic malfunctions are responsible for hypoglycemia but not hypoalbuminemia in dnPKA iEC mice. We also analyzed the expression levels of three key gluconeogenic enzymes, glucose-6-phosphatase (G6pc), pyruvate carboxylase (Pcx1) and phosphoenolpyruvate carboxykinase (Pck1).
Neither the mRNA levels of G6pc or Pcx1 (Figure  8A ) nor the protein levels of Pck1 (Figure  8C, D ) were significantly changed in dnPKA iEC mice compared to control To study whether reduced production of albumin by the liver is responsible for hypoalbuminemia, we determined the hepatic expression of albumin by QPCR and Western blotting. While the level of albumin mRNA was increased (Figure  8A ), the albumin protein level in the liver of dnPKA iEC mice was unchanged compared to the control mice (Figure  8B, D ). Thus, hypoalbuminemia is not a consequence of reduced albumin production by the liver. Rather, drainage of the albumin into the subcutaneous space, as indicated by albumin-binding tracer Evans blue, seems to be the cause for hypoalbuminemia. Taken together, these data demonstrate that hepatic malfunctions are responsible for hypoglycemia but not hypoalbuminemia in dnPKA iEC mice.
Here, 10 control (Tg(Pdgfb-icre/ERT2) 1Frut without floxed transgenes; black) and 5 mutant (red) mice were analyzed (for (C)). For (D), 5 control (black) and 3 mutant (red) mice were analyzed. For panels (C,D), mice (males and females) were injected with tamoxifen at the age of 1-3 days weights, and blood glucose levels were measured starting with the day 40 after injection (40 dpi). dpi, days post injection. Data (C,D) were analyzed using two-way ANOVA followed by Sidak's multiple comparison test. ns, not significant; **, p < 0. 01; ***, p < 0. 001.
Acknowledgments: We would like to thank our colleagues who contributed to this study with their comments, discussions and technical assistance:  Marly Balcer ,  David Cassiman ,  Karolien Claes ,  Lena Claesson-Welsh ,  Lisse Decraecker ,  Willeke de Haan ,  Pauline de Zeeuw ,  Joao Duarte ,  Sarah-Maria Fendt ,  Petya Georgieva ,  Bart Ghesquiere ,  Ulrike Harjes ,  Katleen Lemaire ,  Aernout Luttun ,  Ken Matsumoto ,  Ross J. Metzger ,  Pieter Vermeersch ,  Anne-Clemence Vion ,  Kim Vriens ,  Eddie Wisse ,  Bing Yan  and  Ann Zovein. We also thank  Taija Mäkinen  ( Uppsala, Sweden ),  Sagrario Ortega  ( CNIO, Madrid, Spain ),  Ralf Adams  ( MPI, Münster, Germany ) and  Stanley McKnight  ( University of Washington, Seatlle, USA ) for providing mouse strains used in this study.
We have previously shown that circTAF8 is one of the top ten abundantly expressed circRNAs in chicken leg muscle at three different time points in embryonic development (GSE89355)  (Ouyang et al. , 2018b) , indicating that circTAF8 functions in muscle development. In this study, we assessed the function of circTAF8 in the proliferation and differentiation of primary myoblasts and analyzed the association of the SNPs in the introns flanking circTAF8 with chicken carcass traits. Accordingly, this study aimed to assess whether phenotypic traits are associated with circRNAs and the SNPs in the circRNA-flanking introns.
MATERIALS AND METHODS All animal experiments and sampling procedures used in this study were strictly implemented in accordance with the regulations of the ethics committee of laboratory animals of South China Agricultural University (approval ID: SCAU#2020C030). All samples and carcass-trait data were collected in Guangzhou Kwangfeng Industrial Co. , Ltd. Guangzhou Kwangfeng Industrial Co. , Ltd. , is the animal experimental unit operated under South China Agricultural University.
These obtained data were quantified and analyzed in Microsoft Excel 365 (Microsoft Corporation, Redmond, WA, USA). In addition, four 7-week-old white Recessive Rock (WRR) chickens with similar weights were selected. The heart, liver, spleen, lung, kidney, pectoral, leg chicken, cerebellum, epencephalon, and abdominal fat were collected to characterize the tissue expression profiles of circTAF8 and TAF8.
Genotyping SNPs via Polymerase Chain Reaction Primers based on the TAF8 gene sequence provided by NCBI (accession number: NC_052,555. 1) were designed using the Oligo 7 software version 7. 56 (Molecular Biology Insights, Cascade, CO, USA) and synthesized by Beijing Tsingke Biotechnology Co. , Ltd. (Beijing, China). The information about the primers used is shown in Supplementary Table  S1. PCR was performed in a total volume of 30 μl including the following: 2 μl DNA template, 15 μl 2× Flash PCR Master Mix (CWBio, Beijing, China), 2 μl forward and reverse primers each, and 9 μl ddH 2 O. The PCR program was as follows: pre-denaturation 95 °C for 5 min; 35 cycles of denaturation at 94 °C for 25 s, annealing at 60 °C for 25 s, and extension at 72 °C for 10 s; final extension at 72 °C for 5 min.
5-Ethynyl-29-Deoxyuridine Assay As previously reported  (Cai et al. , 2021) , Edu staining was performed using Cell Light EdU Apollo 488 In Vitro Kit (Ruibo, Guangzhou, China). Briefly, cells were incubated in 50 μM EdU solution for 2 h and then fixed for 30 min using 4% paraformaldehyde solution. Subsequently, 0. 1% Triton X-100 solution was used for cell permeabilization. Finally, the cells were incubated with Hoechst reaction solution at room temperature in the dark for 30 min. A Leica DMi8 fluorescence microscope (Leica Microsystems, Wetzlar, Germany) and ImageJ software (U. S. National Institutes of Health, Bethesda, MD, United States)  (Schneider et al. , 2012)  were used to acquire and analyze the image, respectively.
Flow Cytometric of the Cell Cycle After 48 h of transfection, cells were collected and fixed in 70% ethanol at -20 °C for 24 h. Then, they were centrifuged at 1,000 g for 5 min, and the cell pellets were resuspended in precooled PBS. Subsequently, 0. 5 ml pI/RNase staining buffer (BD Biosciences, Franklin Lakes, NJ, USA) was added to each sample, and the sample was incubated at 37 °C in the dark for 30 min. Flow cytometric analysis was performed on a BD FACSCalibur (BD Biosciences, NJ, USA), and the data were processed using FlowJo software (Treestar, Woodburn, OR, USA).
Immunofluorescence The cells after transfection were washed twice with PBS, fixed in 4% formaldehyde solution for 30 min, and permeated with 0. 1% Triton X-100 at room temperature for 10 min. After blocking with goat serum (Boster, Wuhan, China) for 30 min, the cells were incubated overnight with MyHC antibody (1:50 dilution; DHSB, IA, USA) at 4 °C. After washing 3 times with PBS, the goat anti-mouse IgM/FITC antibody (1:1,000 dilution; Bioss, Beijing, China) was added and incubated at room temperature for 1 h. Finally, the nucleus was stained with a DAPI staining solution (Beyotime, Shanghai, China). A fluorescence microscope and ImageJ software were used to acquire and analyze the image, respectively.
Statistical Analyses The genotype frequency and gene frequency distribution of different alleles were evaluated using Microsoft Excel. The polymorphism information content (PIC) calculation of SNPs sites was based on the formula PIC 1 - n i 1 p 2 i - n-1 i 1 n j i+1 2p 2 i p 2 j Pi and Pj are the frequencies of the "ith" and "jth" alleles, respectively, and n is the number of multiple alleles. PIC >0. 5 indicates high polymorphism, PIC <0. 25 indicates low polymorphism, and 0. 25 < PIC <0. 5 indicates moderate polymorphism. The mixed linear model in SPSS 25. 0 software (IBM, Armonk, NY, USA) was used to conduct an association analysis between different combinations of genotypes and haplotypes of polymorphic sites and chicken carcass traits. The results are shown as "mean ± standard error. " The analysis model is as follows: Y ijlm μ + G i + S j + f l + e ijlm Yijlm is the observed value, μ is the overall average, Gi is the fixed effect of the genotype, Sj is the fixed effect of sex, f l is the random effect of the family, and e ijlm is the random error term.
Bonferroni's test was performed to control for multiple comparisons. In addition, we used online SHesis  (Yong and He, 2005)  and EMBOSS Needle  (Madeira et al. , 2019)  software to analyze the linkage disequilibrium of SNP sequence complementary information, respectively. A priori power analyses were performed using G*Power software version 3. 1. 9. 7 (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany)  (Faul et al. , 2009)  based on the effect size and standard deviation of a previous publication  (Chen et al. , 2021)  or preliminary data to achieve a significance level (α) of 0. 05 and a power of 0. 8. In this study, three biological replicates were included in the analyses unless stated otherwise. The statistical analyses and drawings were performed using GraphPad Prism 8. 0 (GraphPad Software, CA, USA).
05; Figure  1D ). Nuclear and cytoplasmic RNA extraction assays were employed to determine the subcellular localization of circTAF8. U6, β-actin, and GAPDH are used as controls; U6 is mainly distributed in the nucleus, and β-actin and GAPDH are mainly distributed in the cytoplasm. The results showed that circTAF8 is present in both the cytoplasm and the nucleus (Figure  1E ). Next, the expression level of circTAF8 was detected in the Xinghua (XH) and WRR chicken groups by RT-PCR, and the results showed that the expression of circTAF8 in WRR was significantly higher than in XH (p < 0. 05; Figure  1F ). XH and WRR are two breeds with different growth rates. The expression levels of circTAF8 and TAF8 in diverse tissues were quantified using qPCR. The expression profile data showed that the circTAF8 level was highest in the abdominal adipose (Figure  1G ), and TAF8 was the highest in the lung (Figure  1H ).
Association of the SNPs in the Introns Flanking circTAF8 With Carcass Traits To investigate the distribution of SNPs in the introns flanking circTAF8, primers P1, P2, and P3 were used for PCR of a mixed pool of 50 DNA samples, and the PCR products were sent to Beijing Tsingke Biological Co. , Ltd. , for sequencing. The results show that 10 and seven SNP sites were detected in the PCR products obtained from P1 and P2, respectively, and no SNPs were detected in the PCR products of P3. The peak figures of the above sequencing results are shown in Supplementary Figure  S2. All the detected SNP sites are located in the 5ʹ flanking region of the TAF8 gene. Subsequently, direct sequencing was applied to all 335 partridge chicken DNA PCR products amplified with P1 and P2, and the sequencing files were analyzed by SeqMan software (DNASTAR, Madison, WI, USA).
Linkage Disequilibrium and Haplotype Analysis of Eight SNPs circTAF8 Flanking Introns The SHEsis online analysis software was used to analyze the linkage disequilibrium and haplotype of the above eight SNPs. The results showed that g. -1554 T > C and g. -1480A > C can form strong linkage disequilibrium. In addition, the remaining four loci g. -289 C > T, g. -288A > G, g. -210 T > C, and g. -173A > G were in a state of strong linkage disequilibrium (Figure  4 ). A total of seven haplotypes were found in the g. -1554 T > C and g. -1480A > C groups. Only four haplotypes were selected for further analysis, excluding the small individuals (Table  2 ). These four were CCAC (6),  CCCC (191) , TCAC (101), and TTAA (20). Ten haplotypes consist of other four SNPs. Haplotype combinations CCAATCAA (6), CCAATTAA (65), CCAGTCAG (  16 ), CTAGTCAG 135), and TTGGCCGG 82) were used to analyze further the association with carcass traits, except for groups with fewer individuals.
Complementary Pairing Analysis in Flank Sequence of circTAF8 We first used the online software EMBOSS Needle to analyze the sequence complementarity between the flanking introns of circTAF8 (Figure  5A ). The comparison results showed multiple complementary sequences in the two introns. Then, the sequences of the short fragments upstream of the eight SNP sites were aligned with the sequences of the downstream flanking introns (Figure  5B ). The results showed that all SNP sites have short complementary sequences.
, 2020). At present, several research teams have provided databases on the correlation between circRNAs quantitative trait loci (circQTLs) and complex diseases  (Ghosal et al. , 2013; Liu et al. , 2019; Gao et al. , 2021) , but research on the growth traits of animals has not been reported. As a layer of gene regulation network, the expression of circRNAs may be an intermediate phenotype that connects genetic variation and phenotypic change. Given the importance of flanking introns in circRNA transcription level and phenotypic variation, we investigated the polymorphism of flanking introns of circTAF8. The association results showed that a total of eight SNPs were related to carcass traits, and four SNP sites (g. -289 C > T, g. -288A > G, g. -210 T > C, and g. -173A > G) are located within the 150-bp region of the 5ʹ upstream splice acceptor.
Statistical analyses Statistical analyses were performed using the GraphPad Prism software  (version 8. 4. 3 (686) , San Diego, CA, USA). Residual plots for parameters significantly different between groups can be found as Supplementary Figure  S1. The data were subjected to the two-way analysis of variance (ANOVA). Hygiene of housing conditions, RFI line and their interactions were considered as fixed effects. Results are presented as mean ± standard error of mean (SEM). Differences were considered statistically significant at P ≤ 0. 05 and were discussed as a trend for 0. 05 < P < 0. 1.
Acknowledgements The authors are very grateful to the staff of the experimental pig facilities from  INRAE  at  UE3P  ( Saint-Gilles, France ) for animal care and slaughtering. They are also grateful to  Hélène Gilbert  ( INRAE, UMR GenePhySe, Toulouse, France ) for selection of the pig RFI lines. The authors also wish to thank  Frédérique Mayeur  and  Christine Tréfeu  ( PEGASE, Saint-Gilles, France ) for their contribution to the isolation of adipose and muscle cells. Version 3 of this preprint has been peer-reviewed and recommended by  Peer Community In Animal Science  (https://doi. org/10. 24072/pci. animsci. 100011).
Ethical consideration This study was approved by the "Comité de Bioéthique pour la Recherche en Santé (CBRS)" (Bioethics Committee for Health Research) from the Togo Ministry of Health. Participants provided written consent prior to participation. Potential participants were told about the study purpose and procedures, potential risks and protections, and compensation. Informed consent was documented with signed consent forms. All FSW's data were computerized using an identification code and no information that could reveal their identification was entered into the database. In addition, all participants were informed that data collected would be used only for research purposes.
Acknowledgements The authors would like to acknowledge the FSW who accepted to take part in this research and the leaders of the local non-governmental agencies. We would also like to thank the final year medical students of the  Health Sciences Faculty of the University of Lomé  who contributed to data collection. Finally, we acknowledge the  ANRS, "Agence Nationale de Recherche sur le SIDA  et les hépatites virales" for the doctoral scholarship provided for the first author of this work.
An Exhaustive Catalog of Organelle Peptidase Families We searched for homologs of MPP, SPP, PreP, and OOP in 8,340 reference proteomes from Uniprot comprising 291 archaea, 6,820 bacteria, and 1,230 eukaryotes (including 135 photosynthetic eukaryotes). The MPP, SPP, PreP, and OOP sequences of A. thaliana were thus used as a starting point to construct Hidden Markov Model (HMM) profiles with the retrieved homologs (supplementary table  S1 , Supplementary Material online). HMM profiles allowed the detection of 36,807 candidates  (10, 930 PreP; 2, 716 SPP; 6, 822 OOP; and 16, 339 MPP) , from which, only the 26,211 candidates that contain all the Protein Families (PFAM) motifs present in the A. thaliana peptidase of reference were considered its homologs (table  1 ). Given the paucity of those peptidases in the 291 archaeal proteomes present in the Uniprot Reference proteomes, we searched the Uniprot database to retrieve all archaeal sequences in the ca.
440 archaeal species absent from the Reference proteomes that possess either a M16 domain (429 sequences from 143 species) or a M3 domain (1,185 sequences from 623 species) and selected homologs of MPP, PreP, and OOP following the procedure described above. We also searched homologs in the nonredundant (nr) database at NCBI (see Methods). The outcome of this search is provided in supplementary data S1, Supplementary Material online. In total, we found peptidase homologs in <7% of the 1,937 different archaea species with sequences referenced in the three databases. Among the 44 referenced Asgard species, which are considered the most closely related to eukaryotes, only one has an MPP homolog: Candidatus Thorarchaeota archaeon (strain OWC), and another one has an OOP homolog: Heimdallarchaeota archaeon (strain LC_2).
In some bacterial homologs, the M16-M16C region is repeated. Given their heterogeneous distribution on the tree presented in supplementary fig. S3A , Supplementary Material online, it is unlikely that it represents an ancestral form of MPP. 132 additional PFAM domains were identified in our retrieved candidates. These additional domains are heteregeneously distributed and shared by <8 species each, with two exceptions found in the leotiomyceta clade in the Ascomycota phylum (supplementary data S2, Supplementary Material online). The addition of these domains could suggest two independent subfunctionalizations, one in Eurotiomycetes and one in Leotyomycetes. On the SPP tree, homologs from photosynthetic eukaryotes are divided into two subtrees (fig. 4 ). A first one as a sister group of sequences from the superphylum composed of Fibrobacteres, Chlorobi, and Bacteroidetes (FCB) in the lower part of the tree (euka1), and a second one at the basis of the larger group containing the euka1 subtree (euka2).
Among bacteria, PreP is present most exclusively in Terrabacteria (493 homologs) and in Proteobacteria (  263  Material online), containing the cyanobacteria and α-proteobacteria, respectively, the extant bacteria most closely related to chloroplast and mitochondria. The nearly complete absence of homologs from photosynthetic eukaryotes in the NPSeuka subtree suggests that the PreP copy acquired at the time of the mitochondrial endosymbiosis has been lost after the acquisition of the chloroplast, except in five microalgae. The timing of the second PreP acquisition relatively to the chloroplast endosymbiosis is difficult to determine with the present data when considering the Baseuka subtree: half of these Opisthokonta homologs correspond to paralogs from the NPSeuka subtree, supporting an independent duplication before the Opisthokonta divergence followed by rapid evolution, and a second PreP acquisition during or after the chloroplast endosymbiosis.
But the remaining half sequences in the Baseuka subtree have no paralog in the NPSeuka subtree and could also correspond to highly diverged homologs from the Pseuka subtree. This would imply an alternative, but less parsimonious scenario, in which the two acquisitions of PreP occurred before the chloroplast endosymbiosis so that ancestral eukaryotes had two PrePs and one copy has been differentially lost depending on the presence of a chloroplast in the majority of the lineages. In support of this alternative scenario, the domain composition of PreP homologs in the Baseuka subtree is identical to the one in the Pseuka subtree (supplementary fig. S3C , Supplementary Material online). 27 additional PFAM domains were found in PreP candidates, found in <7 species each (supplementary data S2, Supplementary Material online).
The scarce distribution on the archaeal homolog on the MPP and OOP gene trees provide no evidence for their presence in LUCA, but rather for an acquisition from bacteria. Such HGT from Bacteria to Archaea are estimated to be 5-fold more frequent than HGT from Archaea to Bacteria, and have been largely documented, see, for example,  Nelson-Sathi et al. (2015). In the presentation of our results, we assumed that HGT occurred shortly after or during the endosymbiosis, directly from ancestral bacteria close to the extant ones, to the ancestral eukaryotes. This implies that bacterial partners other than the endosymbiont ancestors would have been involved in such events, as proposed earlier  (Gray 2015; FIG. 8. - The heterodimeric yeast MPP. Crystal structure of the yeast MPP  (Taylor et al. 2001, PDB:1hr6)  in two different orientations.
Peptidase Sequences and Proteomes Reference sequences were selected for each of the four peptidases in the photosynthetic model plant A. thaliana: the two members of the PreP family PreP1, Prep2; the three MPP subunits: MPPα1, MPPα2, MPPβ; OOP and SPP, see supplementary table  S1 , Supplementary Material online for accession numbers. The search for homologs was performed against a database composed of the 8,340 nonvirus proteomes from the Uniprot reference database (released version 26 February 2020), including 291 archaea, 6,819 bacteria, and 1,230 eukaryote proteomes.
Family Reconstruction First, the proteome database was searched for significant hits (minimum query coverage of 70% and E-value <10 -5 ) with BLASTP (standalone version 2. 6. 0) (Altschul et al. 1997)  for each of the reference proteases (table 1). In order to retrieve remote homologs from the A. thaliana sequences, we built some HMM profiles for each peptidase with hmmbuild. Hits for the highly similar paralogs PreP1 Evolution of Organelle Processing Peptidase GBE Genome Biol. Evol. 14(7) https://doi. org/10. 1093/gbe/evac101 Advance Access publication 27 June 2022 and PreP2, MPPα1 and α2, MPPβ1 and β2 were merged. Hits were then clustered with the ETE3 toolkit (3. 1. 1), according to the major phylum of Archaea, Bacteria, and Eukaryota of the NCBI taxonomy. Clusters with <5 hits were merged with the clusters from sister phyla.