dataesr/scientific-paragraphs-categorization huggingface dataset:
Homozygosity was computed as the proportion of the autosomal genome belonging to runs of homozygosity, which were defined as ranging ≥1 Mb of length and containing at least 100 SNPs, and were estimated using the homozyg option of PLINK software  (Purcell et al. , 2007). The centromeres were excluded because they are long genomic stretches devoid of SNPs, and their inclusion might inflate estimates of homozygosity if both flanking SNPs are homozygous. The length of the autosomal genome was fixed at 2,673,768 kb as previously  described (McQuillan et al. , 2008). We estimated the selective pressure acting on IFNAR1 to be 2. 762 (indicative of positive selection), by estimating the neutrality index  (Stoletzki and Eyre-Walker, 2011 ) at the population level: (PN/PS)/(DN/DS), where PN and PS are the number of nonsynonymous and synonymous alleles, respectively, at the population level (1000 Genomes Project) and DN and DS are the number of nonsynonymous and synonymous fixed sites, respectively, for the coding sequence of IFNAR1.
Flow cytometry For measuring surface expression on patients' cells, B-LCL and SV40-F cells (5 × 10 5 cells per well) were plated in 96-well plates and surface-stained with either purified mouse anti-IFNAR1 AA3 (provided by Sandra Pellegrini) or PE mouse anti-IFNAR2 (PBL Assay Science) antibodies. Cells stained with AA3 were then washed once with PBS and incubated with a biotinylated rat anti-mouse secondary antibody (Thermo Fisher Scientific) for 30 min before being washed once with PBS and incubated for 30 min with PE-conjugated streptavidin (Thermo Fisher Scientific). The cells were then washed twice with PBS and analyzed by flow cytometry. Data were acquired on an LSRII flow cytometer (BD), and the results were analyzed with FlowJo (TreeStar). For YFV infections, transduced fibroblasts were submitted to IFN-β pretreatment and YFV-17D infection as described below.
Acknowledgments This paper is dedicated to the memory of  Ion Gresser , who passed away in April 2019 (Casanova, J. -L. 2019. J. Interferon Cytokine Res. https://doi. org/10. 1089/jir. 2018. 29015. mem). Ion was a giant in the field of IFN. Among many achievements, he discovered the IFNAR1 studied in this paper. This paper is also dedicated to the memory of  Dr. Reinaldo de Menezes Martins. Dr. Martins , who passed away in January 2019, made immense contributions to the development and study of vaccines, and thereby contributed to thousands of lives being saved. We warmly thank our patients and their families. We thank  T. Kochetkov ,  L. Shang ,  Y. Liang ,  D. Papandrea ,  B. Razooky ,  A. O'Connell , and  Y. Nemirovskaya  for their contributions and all members of the laboratories for fruitful discussions.
We thank  Sandra Pellegrini  for providing IFNAR1 antibodies. We thank  Irina Thiry  for the production of lentiviral particles and transduction of primary fibroblasts. We also thank  Jose German Casas Martin  for assistance with confocal microscopy. This work was supported by  National Center for Research Resources and National Center for Advancing Translational Sciences  grant  UL1TR001866 ; the  National Institute of Allergy and Infectious Diseases (NIAID) for Cooperative Center on Human Immunology  grants  U19AI111825  and  U19AI057229 ;  National Institute of Allergy and Infectious Diseases  grants  R21AI137371  and  R01AI124690 ;  National Vaccine Program Office  of the  US Department of Health and Human Services  grant  VSRNV000006 ; the  St. Giles Foundation ; the  Agence Nationale de la Recherche  under the  "Investments for the Future" program  grant  ANR-10-IAHU-01  and the  Laboratoire d' Excellence Integrative Biology of Emerging Infectious Diseases  ( ANR-10-LABX-62-IBEID );  Institut National de la Santé et de la Recherche Médicale ;  National Immunization Program ;  Institute of Technology in Immunobiology (Bio-Manguinhos ),  Ministry of Health, Brazil ;  KU Leuven  research grant ( GOA/13/013 );  Caps-It research infrastructure  (project  ZW13-02 ) financially supported by the  Hercules Foundation and Rega Foundation ,  KU Leuven ;  Fonds Wetenschappelijk Onderzoek Vlaanderen  grant  G0C8517N ; and  Université Paris Descartes.
, 2000). Bacterial resistance to cationic antimicrobial peptides is nevertheless clearly more difficult to attain than for conventional antibiotics through mutation of the specific target, and it can be considered as a "costly" solution for the bacteria to survive  (Hancock and Sahl, 2006). Moreover, the cross-resistance of laboratory-selected mutants to other peptides seems to be limited  (Samuelsen et al. , 2005) , and the importance of immunomodulatory properties of these peptides, which would not be affected by antimicrobial resistance, has been increasingly recognized  (Hancock et al. , 2016). Taken together, these properties place defensins as a potential therapeutic solution alternative or complementary to conventional antibiotics. However, in spite of an abundant database of antimicrobial peptides 1 , only a few of them have reached phase II-III clinical trials and mainly for topical applications  (Mahlapuu et al.
5 ml) system with a 3 kDa cut-off (Merck Millipore). The degree of labeling was determined according to absorbance measurements at 280 and 750 nm and was of 0. 15, according to the manufacturer's recommendations. Mice were injected by i. p. route with 150 μl of sterile endotoxin-free H 2 O containing (n = 3) or not (n = 2) 100 μg of labeled AvBD7, or containing (n = 3) the same amount of free dye (according to absorbance measurement at 750 nm). The In Vivo Imaging System (IVIS Spectrum, Perkin Elmer) with auto exposure epi-illumination setting (745 nm excitation/800 nm emission) used on anesthetized animal at the indicated time points allowed to record images, according to the manufacturer's recommendations. Living Image 4. 5. 2 software (Perkin Elmer) allowed performing apparatus set up, data acquisition, and data analysis.
ACKNOWLEDGMENTS The authors wish to thank  Corinne Beaugé  and  Marine Chahmanian  from the animal facilities (PFIE  UE1277 ,  Centre INRA Val de Loire, Nouzilly, France ) for technical assistance to experiments on mice. The authors also wish to thank  Nathalie Lallier  ( ISP UMR1282 ,  Centre INRA Val de Loire, Nouzilly, France ) for technical assistance to the preparation of AvBD7. The authors also wish to thank  Prof. Gilles Lalmanach  ( INSERM UMR1100 ,  Centre d' Etude des Pathologies Respiratoires, Equipe « Mécanismes Protéolytiques dans l'Inflammation », Faculté de Médecine, Université de Tours, Tours, France ) for advices on analysis of labeled molecules.
Positive controls with known concentration were included in every run to ensure reproducibility by comparing the expression of VEGF transcripts between different PCR runs. A standard curve was generated by plotting the whole range of series dilution against the initial template quantity; this showed a linear standard curve and an efficiency of amplification near 99%. The absolute quantity of VEGF transcripts (number of copies/μL) in every sample was calculated by comparing the sample Ct value to the standard curve (Standard Curve Quantification method, The LightCycler 480 Software, Roche Diagnostic, France).
Statistical analysis VEGF protein levels were tested for normal distribution using the Kolmogorov-Smirnov test of normality. Consequently, VEGF protein levels were log transformed (log 10 ) to normalize a distribution of data. Data was tested for outliers (1. 5×IQR interquartile range), which were removed before further analysis. Non-parametric correlation analyses (Spearman correlation) were performed. Linear regression models were applied, adjusted for age and gender, to test for possible associations between VEGF protein levels and inflammatory molecules. Associations of specific VEGF mRNA isoforms and inflammatory molecules were assessed using identical statistical procedures. Only VEGF isoform 145 values didn't follow the normal distribution and were log transformed before further analysis.
Mixed models adjusted for family structure were also applied to correct for possible issues of stratification due to familial resemblance. All analyses were performed using SPSS 20. 0 statistical software (SPSS, Armonk, New York: IBM Corp). Significance was determined at a two-tailed 0. 05 level. Graphs for correlation and regression analyses were performed using R 3. 5. 2 statistical software with Jtools package.
Funding: Financial support was provided by the  European Union  within the frames of the  Operational Programme FEDER-FSE Lorraine et Massif des Vosges 2014-2020 , by  Agence Nationale de la Recherche ,  programme d'Investissements d'avenir , grant number  ANR-15RHU-0004  and  GEENAGE  project ( Functional Genomic, Epigenomic and ENvironment interplay Conclusions.
ACKNOWLEDGMENTS This study was supported by  IHU Méditerranée Infection, Marseille, France  and by the  French Government  under the ≪Investissements d'avenir≫ (Investments for the Future) program managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research) , (reference: Méditerranée Infection  10-IAHU-03 ). This work was supported by  Région Provence Alpes Côte d'Azur and European funding FEDER PRIMI. The authors acknowledge  Olga Cusack  for her expert assistance in editing the manuscript. AH benefits from a PhD grant from  Direction des Politiques Scolaires et Universitaires (DPSU) ,  Conseil départemental de Mayotte.
gondii homologs of these components based on the prediction made in (van  Dooren et al. , 2006) (Table S1 -top). We queried the microarray data generated from synchronised T. gondii tachyzoites  (Behnke et al. , 2010)  with these 14 baits. Three genes (TGME49_274090/260850/251780) did not show cyclical expression pattern during the cell cycle, therefore we proceeded with the remaining 11 (Table  S1 ). We queried the microarray data for mRNAs whose pattern of expression correlates with that of each of these 11 baits using either Euclidian distance or Spearmann correlation (as described in the materials and methods section). This search identified 279 genes (Fig. S1A  and Table  S2  -Sheet 1 -column A). Our search criteria were such that each bait was used to query for similar but not identical mRNA expression pattern, namely a search with a given bait does not identify itself.
Despite this restriction, the overall resulting dataset includes 10 of the 11 baits used for the searches (Tables  S1  and S2  -sheet 1 -columns C-D), meaning that queries with some baits identified the other baits, providing validation for this approach. Moreover, three other genes encoding protein import components that were not used in the bait list (since they were not predicted at the time the search was performed) are also found in this dataset (Tables S1 -bottom and S2 -Sheet 1 -columns C-D). As a negative control, we examined 5 lists of 279 T. gondii genes generated at random (by sorting all the protein encoding genes in ToxoDB by size and selecting lists of 279 geneIDs from across the size range -see Table  S2  -Sheet 1 -Columns F-J). We found that the 13 protein import components, and the 10 new mitochondrial proteins we validated in this study below (Table  S3 ), that are all found in our 279 dataset, are underrepresented in the five random gene lists (none of them found in four lists, and five are found in one list) (Table  S2  -Sheet 1 -columns F-P).
These observations provide support for an enrichment of mitochondrial housekeeping proteins in our dataset compared to random datasets of the same size. Moreover, while protein import components and the herein newly identified proteins are enriched, genes encoding mitochondrial proteins that function in other known (metabolic and physiological) pathways that are not expected to co-express with mitochondrial housekeeping pathways are underrepresented in the resulting dataset (Fig. S1B  and Table  S2  -sheet 1 -columns R-AA), providing support to this approach's specificity. To sharpen our focus on mitochondrial translation, we added another filter to our search: we postulated that Cryptosporidium spp, that are the only known species among the apicomplexans that lost their mitochondrial genome, likely lost their mitochondrial translation pathway that is otherwise conserved among other apicomplexans.
We queried the datasets for genes that do not have orthologs in any of the three genomes available for Cryptosporidium spp, but that do have orthologues in the genome of Plasmodium falciparum 3D7. This search identified 43 genes (Table  S2  -sheet 2 -column A). In support of some enrichment of translation components in this dataset, we found 5 of the 35 predicted mitochondrial ribosomal proteins  (Gupta et al. , 2014)  in the 43-gene dataset, while none of them is found in five lists of 43 randomly chosen genes (Table  S2  sheet 2 -columns C-O). We attempted to localise the protein product of 14 genes from this focused dataset. We primarily used endogenous gene tagging via single homologous recombination to introduce a triple-HA epitope tag fusion at the protein c-terminal end  (Huynh and Carruthers, 2009; Sheiner et al.
Mitochondrial targeting predictions were done using MitoProt II (https ://ihg. gsf. de/ihg/mitop rot. html) and Predotar (https ://urgi. versa illes. inra. fr/predo tar/). The presence (Yes) or absence (No) of the gene product in the proximity tagging-based mitochondrial proteome is shown  (Seidi et al. , 2018)  (No -no mass spectrometry data; No* -mass spectrometry data found but the gene is not included in the final list of 421 genes). The fitness score is based on the whole genome CRISPR/CAS9 screen  (Sidik et al. , 2016). to enhance integration of the promoter cassette (Fig. S4 ). The promoter replacement was performed in a parental line where TgmS35 is endogenously tagged with triple-HA (TgmS35-3HA/rTguS15m) and was confirmed using PCR (Fig. 2C ). Western blot analysis showed that downregulation of TguS15m results in depletion of TgmS35 (Fig.
2D ), supporting these proteins being part of the same complex. Finally, we performed immunoprecipitation (IP) experiments with the triple-HA-tagged TgmS35 and used the IP fraction to extract RNA, which was then used as template for RT-PCR. We reasoned that the IP will enrich mitoribosome complexes and, therefore, that mitochondrial rRNA will be enriched in the IP fraction compared to apicoplast rRNA (api-rRNA, using primers from  Biddau et al. , 2018)  or to a cytosolic mRNA (actin, using primers from  Melatti et al. , 2019). The RT-PCR for mitochondrial-encoded rRNA was performed using primers designed to amplify a sequence that is conserved between a coccidian and an haemosporidian whose mitochondrial DNA genome sequences are published (GenBank accessions: M76611. 1 and AB564272. 1) (Fig.
6C ). This data suggests that TOM complex, whose components are not encoded in the mitochondrial genome, continue to assemble. To provide further support for TOM complex assembly, we measured protein   et al. , 2016). We calculated the ratio of signal intensities between the cytosolic un-cleaved protein and the mitochondrial cleaved protein in each condition. We did not observe any significant protein import defect upon TgmS35 depletion, including at the 96 h time point (Fig. 6D ). Taken together, these observations suggest that TgmS35 depletion results in a defect in the assembly and activity specifically of a mitochondrial respiratory complex whose subunits are thought to be encoded in the mitochondrial genome, while other complexes continue to assemble correctly and other mitochondrial biogenesis pathways continue to function.
, 2018). The number of 421 proposed mitochondrial proteins identified is predicted to be markedly smaller than the total number of mitochondrial proteins estimated through comparison to other species  (Panigrahi et al. , 2009; Salvato et al. , 2014; Smith and Robinson, 2018). Many of these potentially missing proteins are expected to target other mitochondrial sub-compartments and thus likely do not possess a canonical mitochondrial targeting signal and cannot be predicted using mitochondrial localisation prediction tools. Here, we used an in silico search resulting in the identification and validation of 10 new T. gondii mitochondrial proteins. These data expand the group of validated mitochondrial proteins by 11%. Only 4 of these 10 are predicted in the  Seidi et al. (2018)  dataset of 421 genes (Table  S3 ), and either 2 or 3 of them are not predicted to target the mitochondrion in two different algorithms (Table  S3 ).
In doing so, our search strategy makes a contribution to the ongoing efforts in the field. Our study focused on mRNAs that co-express with homologs of the mitochondrial protein import machinery components, some of which were recently validated experimentally (van  Dooren et al. , 2016). In the same study the authors identified new components of these import complexes that were not included in our bait list (  van Dooren et al. , 2016). The presence of 3 of these 5 new import components in our 279-gene dataset (Table  S1  bottom) lends support to the power of this strategy to identify mitochondrial protein import components, especially considering none of them are found in five lists of randomly chosen 279 genes (Table  S2  -Sheet 1 -columns F-P). This observation raises the possibility that some other genes in the dataset may take part in protein import.
A previous study showed that at least one of the mitochondrial protein translocation has comparable size to other organisms (  van Dooren et al. , 2016). While homologs of many components known in other organisms are missing in T. gondii  (van Dooren et al. , 2016) , suggesting that currently unknown components are part of this machinery and that they may be parasite specific. The 109 apicomplexan unique genes in our dataset (Table  S6 , column C) provide candidates for those missing pieces. Other characteristics of components of the mitochondrial protein import machinery is absence of a canonical mitochondrial targeting signal, and presence of transmembrane domains. Table  S6  summarises the prediction of these features in the 279 genes. Another pathway whose components may co-express with protein import components is mitochondrial tRNA import  (Esseiva et al.
, 2004; Pino et al. , 2010; Sharma and Sharma, 2015) , as in some organisms these two pathways are linked  (Tschopp et al. , 2011; Seidman et al. , 2012). Mitochondrial tRNA import is likely essential for mitochondrial translation, for which components are enriched in the 43-gene dataset, proposing these 43 genes as candidate for component of this pathway.
In silico search The mRNA abundance dataset generated by  (Behnke et al. , 2010)  was queried via GeneSpring 11. 0. Of the 14 homologs of mitochondrial protein import components (Table  S1 ) 3 do not have cyclical expression pattern (Table  S9  -sheet 1). For each of the remaining 11, we searched for other genes with correlating cyclical expression patterns. We ran Euclid and Spearman correlation tests at 0. 9 and 0. 95 scores and kept lists of correlation that were smaller than 70. This search identified 281 non-overlapping genes (the original search was performed with release 7. 2 of ToxoDB, TGME49 strain; the same group of genes now correspond to 279 predicted genes from this strain) (search steps are shown in Table  S9  -sheet 2). The orthology search leading to the 43 gene list was performed via the ToxoDB link to OrthoMCL.
Protein import assay Parasites were transfected with the Hsp60 L -mDHFR-cMyc vector (van  Dooren et al. , 2016)  (kind gift from Giel van Dooren), after growth in the presence or absence of 0. 5 µM ATc for 24 and 72 h. Transfected parasites were collected after an additional 24 h of growth in their respective treatment and western blot was performed using the respective parasite pellets as described above. Band intensity was measured using the ImageJ software and the ratio between the mature and pre-processed band intensity was calculated. A one-way ANOVA statistical test was performed.
Mass spectrometry Proteins were identified using nanoflow HPLC electrospray tandem mass spectrometry (nLC-ESI. MS/MS) at Glasgow Polyomics. Tryptic peptides, generated using in-gel digest © 2019 The Authors. Molecular Microbiology published by John  Wiley & Sons Ltd, Molecular Microbiology, 112, 1235 -1252  procedure  (Williams et al. , 2018) , were analysed as previously described  (Akpunarlieva et 2017). Protein identities were assigned using the Mascot search engine (v2. 6. 2, Matrix Science) to interrogate protein sequences in the T. gondii genome sequence dataset, ToxoDB 35 release. During result analysis, only peptides with Mascot score of 20 and above (namely the probability that this match might be a random event is 10 -2 or lower) were included in the analysis.
Acknowledgements We thank  Glasgow Polyomics , and  Richard Burchmore  for advice on sample preparation and MS data analysis. We thank  Markus Meissner  for sharing reagents and for productive discussions and advice. We thank  Giel van Dooren  and  Mohamed-Ali Hakimi  for sharing plasmids and antibodies. There are no conflicting interests. LS is a  Royal Society of Edinburgh Personal Research Fellow. The work is funded by  BBSRC   BB/N003675/1  and by  Wellcome Trust Institutional Strategic Support Fund Fellowship  to LS, and by  MVLS Wellcome Institutional Strategic Support Fund (ISSF)   ECR Catalyst Grant  to AEM.
The released actin can then interact with myosin, resulting in stress fiber formation and smooth muscle contraction. In support of this, phalloidin-rhodamine and SM-MHC (green). Relative intensity of rhodamine (F-actin) was quantified using ImageJ software. PKCa inhibitor or Rab35 knockdown substantially reduced IL-17A-induced actin/myosin interaction in ASMCs, and also attenuated IL-17A-enhanced, MCh-induced contraction of ASM. It is important to note in this study that vesicular stomatitis virus G protein-enveloped virus, which was used to knockdown Rab35 may infect other cell types in addition to smooth muscle cells. Therefore, conditional Rab35 KO mice are required for better assessment of cell type-specific contribution of Rab35-mediated trachea contraction. Force generation by ASM is mediated by actin/myosin interaction, which is critically dependent on myosin phosphorylation.
We used the model weights to calculate the % support for each of the fixed effects. We used model averaging to obtain robust parameter estimates and 95% confidence intervals (95% CI) for the fixed effects. A fixed effect is significant when its model-averaged 95% CI does not overlap 0. We used R version 3. 4. 3 to analyse the data. The GLMs and LMEMs were run using the glm() function in the base package and the lmer() function in the lme4 package, respectively. Model selection and calculation of model-averaged parameter estimates were run using the model. sel() and the model. avg() functions in the MuMIn package. Number of engorged nymphs per bank vole. Each bank vole was infested with 3 nymphs, and the number of engorged nymphs collected from each bank vole was therefore a binomial response variable that ranged from 0/3 to 3/3.
Acknowledgements This work was supported by a grant from the  Swiss National Science Foundation (SNSF)  to  Maarten Voordouw  ( FN 31003A_141153 ), as well as the  Kone Foundation , the  University of Jyväskylä , and the  Academy of Finland. We thank  Gaétan Pheulpin  and  Tom Fluri  for help with capturing the bank voles at the site in Neuchâtel. We thank  Barbara Tschirren  for advice on where to capture the bank voles in Zurich and on how to use PHASE v2. 1 to reconstruct the TLR2 haplotypes. Thanks to three anonymous reviewers whose comments improved this manuscript.