dataesr/scientific-paragraphs-categorization huggingface dataset:
Historical Data on Phleboviruses in the Balkans Alois Pick made a clinical description of sandfly fever in Bosnia-Herzegovina military barracks from foreign soldiers at the end of the 19th century (Online Technical Appendix Table ). Pick, an Austro-Hungarian military doctor working in Trebinje (Herzegovina), characterized the syndrome observed in cases of sandfly fever  (26). Sandfly fever was observed both in local populations and in visitors, specifically foreign soldiers. In 1904, Taussig noticed the presence of "pappataci" sand flies in army barracks in Herzegovina and conducted a large clinical and epidemiologic study in the region  (27). The "endemic disease" emerged only in places where sand flies were present. Subsequently, the causative agent was discovered as a filterable agent (virus) that used pappataci sand flies as a vector  (18).
The seminal seroprevalence study using a neutralization assay, by Tesh et al. in 1976, showed that SFNV and SFSV had circulated and were likely to continue to infect human populations in the tested regions (online Technical Appendix Table ). Also in 1976, Gligić et al. isolated a strain of SFNV (Yug Bogdanovac virus strain Yu 8/76) from P. perfiliewi sand flies in the Dobrič region of Serbia. Other strains of SFNV and SFSV were also isolated in Serbia from P. pappatasi sand flies, but no accessible sequence data are available  (24). At the time of isolation, SFNV and SFSV identification was done using mouse hyperimmune ascitis fluid for neutralization assays and acetone sucrose antigens for complement fixation tests. SFNV strain YU-8-76 is available in the Yale University catalog, now stored at the University of Texas Medical Branch at Galveston.
This work was supported in part by the European Virus Archive Goes Global (EVAg) project, which has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 653316. the Authors Dr. Ayhan is a postdoctoral fellow at the Institute of Research for Development in Marseille, France. Her primary research interest is the study of phleboviruses transmitted by sand flies in the Old World. Dr. Charrel is a professor of virology at Aix Marseille University, Marseille, France. His primary research interest is in the field of arboviruses, with specific interest in viruses transmitted by sand flies, and, more generally, study of mechanisms of viral emergence.
This work was supported in part by the European Virus Archive Goes Global (EVAg) project, which has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 653316. the Authors Dr. Ayhan is a postdoctoral fellow at the Institute of Research for Development in Marseille, France. Her primary research interest is the study of phleboviruses transmitted by sand flies in the Old World. Dr. Charrel is a professor of virology at Aix Marseille University, Marseille, France. His primary research interest is in the field of arboviruses, with specific interest in viruses transmitted by sand flies, and, more generally, study of mechanisms of viral emergence. About.
Additional features observed included a heart defect (one patient), mixed (one patient) and sensorineural hearing loss (one patient), and obesity in the eldest patients. The parents of the Iranian patient have a history of multiple pregnancies that resulted in a spontaneous abortion and neonatal death due to respiratory distress accompanied by micromelia. Two other pregnancies were terminated preterm after the detection of short limbs during ultrasound screening. One of these induced abortions presented an additional cleft palate. SLC10A7 mutations and functional analysis. A total of five distinct homozygous SLC10A7 (GenBank: NM_001317816) mutations were identified (Fig. 1h  and Supplementary Fig. 1a ). Two were splice site mutations, located in the acceptor site of exon 10 (c. 774-1 G > A) and in the donor site of exon 9 (c.
773 + 1 G > A), two mutations were a missense substitution located in exon 3 (c. 221 T > C [p. Leu74Pro]) and exon 4 (c. 388 G > A [p. Gly130Arg]), which were predicted to be damaging by the PolyPhen and Sift algorithms, and one inserted a premature stop codon into exon 7 (c. 514 C > T [p. Gln172*]). The two Dutch patients that carried this mutation were genealogically proven to be distantly related through their fathers, while it was not possible to prove a genealogical link between their mothers (Supplementary Fig. 1b ). All mutations segregated with the disease (Supplementary Fig. 1b ) and were not identified in 200 control chromosomes or public databases. SLC10A7 encodes a 10-transmembrane-domain transporter located at the plasma membrane. Three of the mutations are predicted to be null alleles (affecting essential splice sites or generating a premature stop), whereas the missense variant p.
The mean depth of coverage obtained for each sample was ≥ × 80 with 95% of the exome covered at least × 15. Variant calling was carried out with the Genome Analysis Toolkit (GATK)  39  , Sequence Alignment/Map tools  40  and Picard Tools. Single-nucleotide variants were called with GATK Unified Genotyper, whereas indel calls were made with the GATK IndelGenotyper_v2. All variants with a read coverage ≤ × 2 and a Phred-scaled quality of ≤ 20 were filtered out. Variants were annotated and filtered using an inhouse annotation software system (Polyweb, unpublished). The analyses focused on non-synonymous variants, splice variants, and coding indels. Variant pathogenicity was evaluated using the prediction algorithms SIFT (cutoff ≤ 0. 05), PolyPhen-2 (HumVar scores, cutoff ≥ 0. 447) and Mutation Taster (cutoff: qualitative prediction as pathogenic).
The variant frequency in control datasets was assessed, including the ExAC database, dbSNP129, the 1000 Genomes project, ClinVar, HGMD and in-house exome data. All variants were confirmed by Sanger sequencing and segregation was verified. Sequencing analysis of SLC10A7. The exons and exon-intron boundaries of SLC10A7 were amplified with specific primers (Supplementary Table  1 ). Amplification products were purified by ExoSapIT (Amersham) and directly sequenced with the Big Dye Terminator Cycle Sequencing Ready Reaction kit v1. 1 on an automatic sequencer (3500XL and 3130XL; PE Applied Biosystems). Sequence analyses were performed with the analysis software, Sequencing 6 (Applied Biosystems) and Gensearch (PhenoSystems SA). SLC10A7 expression plasmids. Skin primary fibroblasts (control and case 1 and 3) were cultured in RPMI medium supplemented with 10% fetal calf serum.
After mounting in Prolong gold antifade mountant with DAPI (Molecular Probes, Life Technologies) cells were observed with an Axioplan2 imaging microscope (Zeiss). In situ hybridization. Probes for SLC10A7 and Slc10a7 corresponded to nucleotides 46-948 of GenBank accession NM_001029998. 5 and nucleotides 429-717 of GenBank accession NM_001009981. 2, respectively. Synthetic cDNAs (Eurofins Genomics) were used to generate antisense and sense cRNA probes using a SP6/T7 DIG RNA labelling kit (Roche) and digoxigenin-11-UTP (Roche) according to the manufacturer's specifications. Paraffin sections of paraformaldehyde-fixed human fetuses at 6 and 7 weeks of gestation (Carnegie stages 16 and 19, respectively; obtained with institutional review board approval), murine wild-type embryos at embryonic age E12.
Cells were washed three times with Ca2 + -free buffer and then kept in Ca 2+ -free buffer (140 mM NaCl, 4 mM KCl, 1 mM MgCl 2 , 25 mM glucose and 10 mM HEPES, pH 7. 4). Under fluorescent microscopy (Nikon Instruments Eclipse Ti Inverted Microscopes, NIS-Elements Microscope Imaging software), in Ca 2+ -free buffer, CaCl 2 (20 mM) was then added extracellularly to facilitate Ca 2+ influx. Peak fluorescent intensities following CaCl 2 were captured and average values from at least two sets of cells per patient or control were obtained for analysis. Data were analysed as the percentage of fluorescence intensity before Cacl 2 for each cell. Glycoprotein electrophoretic profile assay. For analysis of glycoproteins from blood spots on Guthrie cards, one circular punched spot was first eluted in 100 μl of distilled water.
For timed pregnancies, the date of vaginal plug was designated E (embryonic age) 0. 5. For all analyses of Slc10a7 +/-and Slc10a7 -/-mice, wild-type littermates were used as controls, and both male and female were used at birth, 10 days of age and 8 weeks of age, as indicated. Skeletal staining. Newborn skeletons were skinned, fixed in 95% ethanol, stained with Alcian Blue and Alizarin Red, cleared by KOH treatment and stored in glycerol according to standard protocols. Images were captured with an Olympus SZX12 stereo-microscope. Bone sizes were measured using ImageJ software. X-rays and μCT. Whole mouse skeletons were radiographed using a Faxitron (MX-20). For μCT analyses adult animals were euthanized and femurs and heads were isolated, stripped of soft tissue and stored in 70% ethanol.
Three-dimensional microarchitecture of the distal femur was evaluated using a high-resolution Sky-scan1076 microtomographic imaging system (Skyscan, Belgium). Images were acquired at 80 KeV, 100 μA, no filter. Three-dimensional reconstructions were generated using NRecon software (Skyscan). Trabecular and cortical measurements were obtained using CTan software (Skyscan) on a set of sections located within the secondary spongiosa under the growth plate and under the secondary spongiosa, respectively. The measured volume was chosen to be proportional to the femur length. Avizo software (FEI Visualization Sciences Group, Burlington, MA) was used for three-dimensional visualization of heads and for mandible, incisor and molars selection and volume quantification. Scanning electron microscopy analysis of incisor enamel.
Specific signal intensity was measured with ImageJ software, selecting equivalent areas of the growth plate in each group. Statistics. Statistical analyses were performed using GraphPad PRISM. All values are shown as mean ± SD. Statistical differences between two groups were analysed with a two-tailed Student's t-test, assuming a normal distribution. A p-value of < 0. 05 was considered statistically significant. URLs. For 1000 Genomes, see http://www. 1000genomes. org. For ExAC Browser, see http://www. exac. broadinstitute. org/. For GeneMatcher, see http://www. genematcher. org. For GenBank, see http://www. ncbi. nim. nih. gov/genbank/. For PolyPhen-2, see http://genetics. bwh. harvard. edu/pph2. For SIFT, see http://www. sift. jcvi. org. For UCSC Genome Browser, see http://www. genome. ucsc.
Acknowledgements This work was supported by the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement number  602300  (SYBIL) and the  Fondation pour la Recherche Médicale (FRM)  funding ( DEQ20120323703 ). We gratefully acknowledge  Myriam Oufadem , the  Genomic and Bioinformatic facilities of Institut Imagine  for help in exome sequencing analyses,  Morad Bensidhoum  and  SFR IMOSAR  for μCT scans,  Benjamin Fournier  from  Laboratory of Molecular Oral Pathophysiology  ( INSERM UMR_S1138 ) for scanning electron microscopy analysis,  Barbara Vona  and  Thomas Haaf  from  Institute of Human Genetics of Julius Maximilians University Würzburg (Germany), Eric Hennekam from the department of genetics (University Medical Center, Utrecht ) and the medical specialists in the  UMC Utrecht Expert Center for Congenital Orofacial and Dental Anomalies  for sharing patient data, and the Histology facility and the  Imaging facility of SFR Necker.
Because we were interested in genes with a strong fitness defect (significant negative fitness values), we first removed genes with an absolute t-score <= 3. This t-score threshold was set to identify strong negative fitness values while minimizing potential false positives (false discovery rate of 0. 2%). The t-score assesses how reliably a fitness value is different from 0. In each condition, most genes have no detectable fitness effect, and thus have a fitness value close to 0. Thus, in our dataset, most of the genes below this t-score also have a fitness value close to 0. On average, 97% of the fitness values were associated with a t-score that falls below our threshold. Within the fitness values that pass the t-score threshold, we then removed genes associated with positive fitness values.
Thus, we only retained the genes whose deletion leads to a consistent growth defect for E. coli on CCA compared to the rest of the library. This filtering process revealed 160 genes that were important for E. coli growth alone on CCA (Figure  1A ). To identify the functions associated with these 160 genes, we mapped them to the KEGG BRITE database (Figure  1B ). 84 genes were assigned to KEGG modules and 64 of them were associated with E. coli metabolism. Within these metabolic genes, we found 28 genes associated with amino acid metabolism, specifically the biosynthesis of all amino acids except for proline, lysine and histidine. Quantification of free amino acids in our medium highlighted very low concentrations of all amino acids (Figure  1 -figure supplement 2) suggesting that a limited supply of free amino acids leads to a genetic requirement for amino acid biosynthesis.
A t-score threshold of absolute(t-score)!3 was used to identify genes with strong fitness effects. 97% of the genes fell below this threshold and have no strong and significant fitness effect. Black dots represent genes with strong negative fitness effects. Altogether, they represent 160 different genes that are associated with a significant fitness value for at least one timepoint. (B) These 160 genes were mapped to the KEGG BRITE Database for functional analysis and identification of required functions for E. coli growth on CCA. 84 of the 160 genes had hits when mapped to the KEGG BRITE database. In summary, functions of major importance for E. coli to grow alone in our experimental environment involved (i) response to low iron availability, (ii) response to osmotic stress and (iii) response to limited available nutrients (specifically free amino acids).
Just over half of the genes with negative fitness alone appeared to be relieved by the presence of a partner (n = 82 genes, Figure  2C ), suggesting major modifications of growth conditions following the introduction of a partner. We mapped these 82 alleviated genes to the KEGG BRITE database to identify functions and pathways that are no longer critical in the presence of a partner (Figure  2E ). 16 genes were associated with unknown or predicted proteins and did not map to any field of the database. Of the remaining genes, 45 mapped to modules of the KEGG orthology hierarchy. Most of the genes with alleviated negative fitness were associated with the KEGG metabolism module and are thus part of metabolic pathways. It is especially evident that pairwise growth leads to major changes in the need for amino acid biosynthesis.
Differential expression analysis of E. coli in interactive conditions versus growth alone So far, we used a genome-scale genetic approach to investigate potential microbial interactions. As a complementary strategy, we generated transcriptomic data for E. coli during growth in each previously described condition. Changes in transcriptional profiles can be a powerful indicator of an organism's response to an environment and have been used to identify E. coli pathways involved in interactions  (Croucher and Thomson, 2010; McAdam et al. , 2014; Galia et al. , 2017). To measure E. coli gene expression, we extracted and sequenced RNA from each timepoint and condition of the same samples used for RB-TnSeq above (after 1, 2 and 3 days of growth when grown alone, in pairwise conditions or with the community).
Then, we performed functional enrichment analysis on KEGG pathways for pairwise-downregulated genes, community-downregulated genes, pairwise-upregulated genes and community-upregulated genes. Functional enrichment was performed using the R package clusterProfiler  (Yu et al. , 2012)  and only the KEGG pathways enriched with an adjusted p-value lower than 5% (Benjamini-Hochberg correction for multiple testing) were considered. (C) We compared pairwise-upregulated genes with community-upregulated genes and pairwise-downregulated genes with community-downregulated genes to identify if expression regulation from pairwise conditions is conserved in the community context and if we observe specific changes in pairwise or community conditions. (D) Within the genes specifically upregulated during growth with the community, we observed the upregulation of multiple genes associated with the nitrogen starvation response.
, 2017). Illumina data was then used to correct sequencing error using the software Pilon  (Walker et al. , 2014). The assembled genome was annotated using the Integrated Microbial Genomes and Microbiomes (IMG/M) system  (Markowitz et al. , 2012). The P. psychrophila JB418 genome is 6,072,477 nucleotides long. It contains a single circular chromosome of 5. 85 Mb and 4 plasmids of 172. 2 Kb, 37. 7 Kb, 5. 8 Kb and 2. 4 Kb. 6060 genes including 5788 open reading frames were identified. This genome is publicly available on the IMG/M website as IMG Genome ID 2751185442. Transposon mutant library construction in P. psychrophila JB418 P. psychrophila JB418 was mutagenized by conjugation with E. coli strain APA766 (donor WM3064 which carries the pKMW7 Tn5 vector library containing 20 bp barcodes)  (Wetmore et al.
Data processing and fitness analysis BarSeq data processing and gene fitness calculation were performed separately for the E. coli and the P. psychrophila JB418 experiments. For each library, BarSeq reads were processed using the Perl script BarSeqTest. pl from  (Wetmore et al. , 2015). This script combines two Perl scripts essential for the BarSeq data processing. After the raw reads have been de-multiplexed, the computational pipeline: (i) identifies individual barcodes and the associated number of reads, (ii) calculates the strain fitness for each insertion mutant and (iii) calculates the normalized fitness value for each gene along with a t-statistic value (t-score). The following parameters were applied during the fitness calculations: (i) only insertion mutants located within the central region of genes (10%-90%) were considered, (ii) barcodes with less than three reads in the T0 were ignored and (iii) genes with less than 30 counts across all barcodes in T0 were ignored.
For each library, the pipeline uses a table where each barcode is mapped to a location in the genome. The Arkin lab (Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA) kindly provided the TnSeq table for the E. coli library and we generated a TnSeq table for P. psychrophila strain JB418. The different scripts used for this analysis originate from  (Wetmore et al. , 2015)  and are publicly available on https://bitbucket. org/berkeleylab/feba. We calculated E. coli and P. psychrophila JB418 genes fitnesses at T = 24 hr (Day1), 48 hr (Day2) and 72 hr (Day3) in the following conditions: growth alone, growth with H. alvei, growth with G. candidum, growth with P. camemberti and growth with the community. First, strain fitness for each insertion mutant that met the criteria described above is calculated as the log2 of the ratio of the insertion mutant's abundance at the time of the harvest (number of reads of the associated barcode) and its abundance in the T0 sample.
A t-score is calculated along with each gene fitness to evaluate how reliably different from zero the gene fitness is. The t-score is a moderated t-statistic calculated as the ratio of the gene fitness and its standard deviation. More details can be found in  Wetmore et al. , 2015. In this study, all our experiments and genes fitness values met the quality requirements to be further analyzed (Figure  1 -figure supplement 7). analysis using the package DESeq2. Differentially expressed genes between two conditions were selected with an adjusted p-value lower than 1% (Benjamini-Hochberg correction for multiple testing) and an absolute log2 of fold change equal to or greater than 1.
KEGG pathway enrichment analysis Functional enrichment analysis was performed using the R package clusterProfiler  (Yu et al. , 2012). We used the latest version of the package org. EcK12. eg. db for E. coli annotations (R package version 3. 5. 0. ). We used Benjamini-Hochberg for multiple comparison correction and only the KEGG pathways enriched with an adjusted p-value lower than 5% were considered.
Acknowledgments The authors would like to thank: the  Arkin lab  and the  Deutschbauer lab at UC-Berkeley  for the E. coli Keio_ML9 library and their help for the RB-TnSeq analysis,  Steven Villareal  and  Tyler Nelson  for their help in the competitive assays,  Kristen Jepsen  at the  IGM Genomics Center at the University of California San Diego  for assistance with sequencing,  Ben Wolfe  and  Sandeep Venkataram  for their constructive comments on this work as well as all the  Dutton lab  members for their input. Nanopore sequencing of P. psychrophila JB418 was supported in part by  NSF  Grant No. PHY17-48958 ,  NIH  Grant No. R25GM067110 , and the  Gordon and Betty Moore Foundation  Grant No. 2919. 01.
For each gene, we display the number of sequencing reads for associated insertion mutants in the T0 sample, in growth alone day 3 and growth with G. candidum day 3. These sequencing reads are the raw data accounting for mutant abundance and used for fitness calculation (f represents each gene's fitness value). While reads are not rarefied in the fitness calculation pipeline, we used rarefied reads for the purpose of the figure. Asterisks indicate genes with significant fitness values (consistent decrease in the number of reads per insertion mutant in the condition compared to T0). (E) We mapped the genes associated with conserved, pairwise-induced, and pairwise-alleviated negative fitness to the KEGG BRITE database. 41/75, 45/82 and 33/77 genes had hits. DOI: https://doi. org/10. 7554/eLife.
candidum day 3. These sequencing reads are the raw data accounting for mutant abundance and used for fitness calculation (f represents each gene's fitness value). While reads are not rarefied in the fitness calculation pipeline, we used rarefied reads for the purpose of the figure. Asterisks indicate genes with significant fitness values (consistent decrease in the number of reads per insertion mutant in the condition compared to T0). (E) We mapped the genes associated with conserved, pairwise-induced, and pairwise-alleviated negative fitness to the KEGG BRITE database. 41/75, 45/82 and 33/77 genes had hits. DOI: https://doi. org/10. 7554/eLife. 37072. 013The following source data and figure supplements are available for figure2:Source data 1. RB-TnSeq analysis of E. coli's growth in pairwise conditions on 10% cheese curd agar, pH7.
(B) We regroup any genes upregulated in at least one pairwise condition as a single set of pairwise-upregulated genes and did the same for pairwise-downregulated genes. Then, we performed functional enrichment analysis on KEGG pathways for pairwise-downregulated genes, community-downregulated genes, pairwise-upregulated genes and community-upregulated genes. Functional enrichment was performed using the R package clusterProfiler (Yu et al. , 2012)  and only the KEGG pathways enriched with an adjusted p-value lower than 5% (Benjamini-Hochberg correction for multiple testing) were considered. (C) We compared pairwise-upregulated genes with community-upregulated genes and pairwise-downregulated genes with community-downregulated genes to identify if expression regulation from pairwise conditions is conserved in the community context and if we observe specific changes in pairwise or community conditions.