dataesr/scientific-paragraphs-categorization huggingface dataset:
To perform a combined analysis of our gene expression data and the GWAS results, we had to define pairs of genes (for which we have expression data) and SNPs (for which we obtained GWAS P values). Thus, each gene in our expression data was coupled with the GWAS SNP with the lowest p-value among all tested SNPs located within 50 kb of the gene's transcription start site (Supplementary Data S4; this gene-SNP definition was performed separately for each GWAS data set). Once we defined gene-SNP pairs, we asked whether differences in gene expression levels between putatively resistant and susceptible individuals could help us identify genetic variation that is associated with susceptibility to TB. In other words, we asked whether increasing evidence for DE genes is associated with low GWAS p-values.
Second, we collected samples from a larger number of putatively resistant individuals (19 versus 4), increasing our power to distinguish between the gene expression profiles of susceptible and resistant individuals. Third, they measured gene expression with microarrays; whereas, we used RNA-sequencing. Considering the substantial technical differences between the methods used and the biological differences between DCs and macrophages  26, 48  , unsurprisingly, we were unable to identify the susceptible individuals from Thuong et al. 20  using our classifier (Supplementary Fig. S17 ). Indeed, at this time, we are not aware of any other data set from healthy individuals known to be susceptible to TB, with which we can further test our classifier. When we applied our classifier to an independent set of non-infected DCs isolated from healthy individuals of unknown susceptibility status, our model predicted that 7.
We designed the batches for RNA extraction, library preparation, and sequencing to balance the experimental factors of interest and thus avoid potential technical confounders (Supplementary Fig. S1 ). Read mapping. We mapped reads to human genome hg38 (GRCh38) using Subread  49  and discarded non-uniquely mapping reads. We downloaded the exon coordinates of 19,800 Ensembl 50 protein-coding genes (Ensembl 83, Dec 2015, GRCh38. p5) using the R/Bioconductor 51 package biomaRt  52, 53  and assigned mapped reads to these genes using featureCounts  54. Quality control. First we filtered genes based on their expression level by removing all genes with a transformed median log 2 counts per million (cpm) of less than zero. This step resulted in a set of 11,336 genes for downstream analysis (Supplementary Fig.
Combined analysis of gene expression data and GWAS results. The GWAS p-values were from previously published studies of TB susceptibility conducted in Russia  18  , The Gambia 13 , Ghana  13  , and Uganda and Tanzania  19  (and a height GWAS in individuals of European descent  25  ). To perform a combined analysis of the gene expression and the summary statistics from each GWAS, we assigned each gene to the SNP with the minimum GWAS p-value out of all the SNPs located within 50 kb up or downstream of its transcription start site. Specifically, we obtained the genomic coordinates of the SNPs with the R/Bioconductor 51 package SNPlocs. Hsapiens. dbSNP144. GRCh38 and matched SNPs to nearby genes using GenomicRanges 61. 10,265 to 11,060 of the 11,336 genes were assigned an association p-value depending on the GWAS (Supplementary Data S4).
For each of the 4 differential expression contrasts we tested (resistant vs. susceptible individuals in the non-infected state, resistant vs. susceptible individuals in the infected state, effect of treatment in resistant individuals, effect of treatment in susceptible individuals), we also performed an enrichment analysis. To do so, we calculated the fraction of genes assigned a GWAS SNP with p-value less than 0. 05 for bins of genes filtered by increasingly stringent cutoffs for the observed differential expression effect size (the absolute value of the log fold change). The effect size cutoffs were chosen such that on average each subsequent bin differed by 25 genes. To measure enrichment, we calculated the area under the curve using the R package flux  62  (we also subtracted the background area under the line y = 1 because the number of genes assigned a SNP varied across the GWAS).
Classifier. The training set included data from the 22 high-quality non-infected samples from this study with known susceptibility status. The test set included the 65 non-infected samples from one of our previous studies in which the susceptibility status is unknown  24  , and thus assumed to be similar to that in the general population (10%) (we also tested the classifier on data from a small study of macrophages  20  , Supplementary Fig. S17 ). Because the two studies are substantially different, we took multiple steps to make them comparable. First, we subset to include only those 9,450 genes which were assayed in both. Second, because the dynamic range obtained from RNA-seq (current study) and microarrays (previous study  24  ) were different, we normalized the gene expression levels to a standard normal (μ = 0, σ = 1) distribution (Supplementary Fig.
Cutoffs of 5%, 10%, 15%, 20%, and 25% corresponded to gene set sizes of 99, 385, 947, 1,934, and 3,697, respectively. We used the R package caret  63  to train 3 different machine learning models: elastic net  64  , support vector machine  65  , and random forest  66  (the parameters for each individual model were selected using the Kappa statistic). To assess the results of the model on the training data, we performed leave-one-out-cross-validation (LOOCV). In order to choose the model with the best performance, we calculated the difference between the mean of the LOOCV-estimated probabilities of being TB resistant for the samples known to be TB resistant and the corresponding mean for the samples known to be TB susceptible. This metric emphasized the ability to separate the susceptible and resistant individuals into two separate groups.
Software implementation. We automated our analysis using Python (https://www. python. org/) and Snakemake  67. Our processing pipeline used the general bioinformatics software FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/), MultiQC  68  , samtools  69  , and bioawk (https://github. com/lh3/bioawk). We used R  70  for all statistics and data visualization. We obtained gene annotation information from the Ensembl  50  and Lynx 71 databases. The computational resources were provided by the University of Chicago Research Computing Center. All code is available for viewing and reuse at https://github. com/jdblischak/tb-suscept.
Acknowledgements We thank  Matthew Stephens  and  John Novembre  for providing feedback and Gilad lab members for helpful discussion. We thank  Marie-Noëlle Ungeheuer  and  Nathalie Jolly  for help recruiting subjects. We thank  Sergey Nejentsev  for sharing data from the GWAS in Russia. We thank  Thorsten Thye  for sharing data from the GWAS in Ghana and The Gambia. We thank  Rafal S. Sobota ,  Catherine M. Stein ,  Giorgio Sirugo , and  Scott M. Williams  for sharing data from the  GWAS in Uganda and Tanzania. This study was funded by  National Institutes of Health (NIH)  Grant  AI087658  to Y. G. and L. T. J. D. B. was supported by  NIH  Grant  T32GM007197. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  NIH.
Data availability. The raw fastq files have been deposited in NCBI's Gene Expression Omnibus  72  and are accessible through GEO Series accession number GSE94116 (http://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?ac-c=GSE94116). The RNA-seq gene counts and other summary data sets are included as Supplementary Data and are also available for download at https://github. com/jdblischak/tb-suscept. While a small subset of the GWAS summary statistics required for partially reproducing our results were included in Supplementary Data S4, we do not have permission to share the full set of summary statistics from the previously published TB susceptibility GWAS that would be required for fully reproducing our results. To access the summary statistics, contact the authors directly: Russia -Sergey Nejentsev (sn262@cam.
Fractionation and Mass Spectrometry HES was separated into 1 ml fractions by size exclusion chromatography using a Superdex 200 10/300 GL column, or by anion exchange chromatography using a MonoQ 5/50 GL column (GE Healthcare) in a 40 column volume gradient from 20 mM TrisHCl pH 8 (start buffer) to a maximum of 30% 20 mM TrisHCl + 1 M NaCl pH 8 (elution buffer). All fractions were trypsinized and analyzed by LC MS/MS on an on-line system consisting of a capillary-pump Agilent 1200 HPLC system (Agilent, UK) coupled to an Orbitrap XL mass spectrometer (Thermo Scientific) as previously described  (Hewitson et al. , 2011; Hewitson et al. , 2013). LC MS/MS data was analyzed using Mascot (v2. 4, Matrix Science) and searched against an improved in-house BLASTx annotated database obtained by 454 sequencing of H.
polygyrus adults, with additional full length H. polygyrus sequences from NCBI, WormBase ParaSite  (Howe et al. , 2016)  and our own Sanger sequencing (Harcus Y. et al, manuscript in preparation). Peptides identified were ranked by Mascot protein score, with a minimum cutoff score of 20, with a significance threshold of p<0. 05. Protein abundance was estimated by emPAI (exponentially modified protein abundance index).
Bioinformatics Characterization and Modeling Domain identification and assignment were undertaken using a combination of SMART  (Letunic et al. , 2015) , an HHPred search against the pdb70 database (accessed March 2016)  (Berman et al. , 2000; So ¨ding, 2005) , and refined manually based upon positioning of the four Cysteine residues that typify CCP module sequences  (Soares et al. , 2005). PROSITE  (de Castro et al. , 2006)  was used for short motif searches. ESPript v3  (Robert and Gouet, 2014)  was used for alignment figure preparation. The three predicted HpARI CCP module sequences were modeled based upon their top ranked CCP module template structure 'hits' as suggested by HHPred. HpARI-CCP1 was modeled based upon CR2-CCP2 (PDB ID: 1LY2)  (Prota et al. , 2002)  (after a manual switch of Leu 69 with Trp 69 to help identify this CCP module using HHPred; note Leu/Trp substitutions exist in other experimentallydetermined CCP module structures such as complement Factor H CCP10 and CCP20  (Makou et al.
, 2012; Morgan et al. , 2012) ; HpARI-CCP2 on CSMD1-CCP3 (PDB ID: 2EHF) (RIKEN Structural Genomics/Proteomics Initiative); HpARI-CCP3 on GABABR1a-CCP2 (PDB ID: 1SRZ)  (Blein et al. , 2004). The target-template alignment in each case was based upon the initial HHPred alignment, then extended to include the first Cysteine residue in each domain, realigned using ClustalX  (Thompson et al. , 1997) , and finally subjected to manual editing to optimally position known consensus residues, secondary structure elements and gaps  (Soares et al. , 2005). Note, an alternative alignment for the atypical insertion in CCP3 is possible where it can be accommodated after the hypervariable loop (not shown). A total of 100 models for each CCP module were built using Modeller v9. 12  (Sali and Blundell, 1993) , and the model with the lowest DOPE  (Shen and Sali, 2006)  energy score selected as the representative model in each case and evaluated for valid stereochemistry  (Lovell et al.
ACKNOWLEDGMENTS H. J. M. is supported by an  Asthma   UK Fellowship  ( SPD-2012-172 ) and a  Medical Research Council Confidence in Concept award  ( MRC/CIC4/03 ), R. M. M. is supported by the  Wellcome Trust  through an  Investigator Award  ( Ref 106122 ) and core funding to the  Wellcome Centre for Molecular Parasitology  ( Ref 104111 ). The EPPF is supported by the  Wellcome Trust   Multi-User Equipment grant   101527/Z/13/Z. E. S. C. and I. C. S. are employees of the  AstraZeneca Group  and have stock/stock options in AstraZeneca. We thank the  Lothian NRS Bioresource  for supplying human lung material.
Results Detection, identification and quantification of a protein expressed at low CO 2. Proteins in the crude extract obtained at different times after switching cells from high to low CO 2 were analysed by one dimensional SDS-PAGE. There were no striking changes in protein expression, apart from a decrease of the large subunit of Rubisco (arrow 2, Fig. 1 ) and an increase of a protein with a molecular mass of about 60 kDa (arrow 1, Fig. 1 ) that was detected approximately 6 h after the switch (Fig. 1 ). This protein was also present at 400 ppm (data not shown). The band at about 60 kDa induced at low CO 2 , and also the slice of gel at the same position from the extract at high CO 2 , but without a band (control), were excised and analysed using mass spectrometry. Using GENSCAN software (http://genes.
This confirmed that the 68. 7 kDa protein sequence contains an N-terminal endoplasmic reticulum peptide signal of 22 amino acid residues (Fig. 2 , green) and a chloroplast transit peptide of 34 amino acid residues (Fig. 2 , orange; confirmed using Hectar software) and consistent with the transit peptide sequences for diatoms found before  31, 32. Sequence analysis of LCIP63 revealed four COG4875 related domains (uncharacterized protein conserved in bacteria with a cystatin-like fold) which are similar to Calcium/calmodulin dependent protein kinase II Association Domain (CaMKII_AD; Supporting data, Fig. S1 ). This protein sequence was also found in other marine diatoms, for example, Thalassiosira oceanica (four domains), Pseudo-nitzschia multiseries (three domains) and P. tricornutum (two domains) (Supporting data, Figs S2 to S4).
LCIP63 was identified using a Synapt G1 mass spectrometer (Waters, Manchester, UK) coupled to a nano flow UPLC nanoAcquity (Waters). Spectra and protein search were processed by the PLGS 3. 0. 1 software (Waters) with the same search parameters as described below. N-terminal sequence determination was performed by Edman degradation using an automatic sequencer (Procise 494, Applied Biosystems). Mature sequence of LCIP63 was analysed using Hectar (http://webtools. sb-roscoff. fr/) to identify the location of the protein. RNA quantification. T. pseudonana cells were centrifuged at 3,720 g for 10 min at 4 °C, frozen with liquid nitrogen and stored at -80 °C. 100 mg of cells, were incubated with 4 mL of Trizol ® reagent (ThermoFisher scientific, Waltham, USA), for 5 min and 800 μ L of chloroform (Carlo Erba, Milan, Italy) was added.
The reaction volume was 15 μ L and the final concentration of each primer was 0. 5 μ M. The cycling parameters of the qPCR were 98 °C for 2 min, followed by 45 cycles of 98 °C for 5 s, 60 °C for 10 s. A final melting curve from 65 °C to 95 °C was performed to determine the specificity of the amplification. To determine the amplification kinetics of each product, the fluorescence derived from the incorporation of EvaGreen into the double-stranded PCR products was measured at the end of each cycle using the SsoFast EvaGreen Supermix 2X Kit (Bio-Rad, USA). The results were analysed using Bio-Rad CFX Manager software, version 3. 0 (Bio-Rad, USA). The RNA actin gene was used as a normalization reference. For each point, a technical duplicate and biological triplicate was performed. The amplification efficiencies for each primer pairs were between 75 and 100%.
IPG strips were placed on the top of the precast gels, overlaid with 0. 5% agarose in 2x TGS running buffer (Biorad) containing bromophenol blue. Gels were run at 20 °C using TGS 1x running buffer. Electrophoresis was conducted at 50 V for 5 h. After SDS-PAGE, cyanine dye-labeled protein gels were scanned directly using the EDI scanner (GE Healthcare) at a resolution of 50 μ m. After fluorescence imaging, gels were washed for 10 min in ultrapure water and post-stained with the ImperialTM Protein Stain (Thermo Scientific, Rockford, USA). The Samespot software package (version 4. 0. 3; nonlinear dynamics) was used to determine protein abundance and statistics based on 2D-DiGE. Based on Samespot analysis, spots of interest were excised using a Shimadzu Biotech Xcise System (Champs sur Marne, France) and submitted to trypsin digestion after reduction and alkylation.
Search parameters were: T. pseudonana extracted from the protein database NCBI (ID 35128, 24,090 entries); Enzyme trypsin ;maximum two miscleavages;carbamidomethylation of cysteine and oxidation of methionine set as static and dynamic modifications, respectively; precursor and fragment mass tolerance set at ± 5-10 ppm and ± 0. 02 Da, respectively. Proteins were identified if 2 unique "rank 1" peptide sequences of more than 6 amino acids passed the high confidence filter, with validation on q-Value (Strict Target FDR: 0. 01) and maximum Delta Cn: 0. 05 (Sequest HT criteria), or the peptide individual score was above 20 and p < 0. 05 (Mascot criteria). Enzyme activities measurement. Enzyme activities were measured using extracts from exponentially growing cells. The soluble protein extracts were prepared as described in Erales et al.
Acknowledgements We dedicate this paper to our co-author, colleague and dear friend,  Sabrina Lignon , who died at the age of 36, before this work was completed. Romain Clément 's studentship was supported by the  Ministère de l'Education Nationale, de la Recherche et de la Technologie (MENRT). Brigitte Gontero 's group is supported by  Centre National de la Recherche Scientifique ,  Aix-Marseille Université , A*midex project (No. ANR-11-IDEX-0001-02 ),  Agence Nationale de la Recherche  ( Signaux-BioNRJ ,  ANR-15-CE05-0021-03 ), the  Region PACA and IBiSA. Stephen Maberly 's work is supported by the  UK Natural Environment Research Council  and a visiting scholarship from  Aix Marseille Université. We thank  Ahmed Zellat  for technical support.
measurements, the IAGOS database is a unique data set to derive trends in the UTLS at northern mid-latitudes. Trends in  O 3  in the UT are positive and statistically significant in most regions, ranging from +0. 25 to +0. 45 ppb. yr -1 , characterized by the significant increase of the lowest values of the distribution. No significant trends of O 3 are detected in the LS. Trends of CO in the UT, tropopause and LS are all negative and statistically significant. The estimated slopes range from -1. 37 to -0. 59 ppb. yr -1 , with a nearly homogeneous decrease of the lowest values of the monthly distribution (fifth percentile) contrasting with the high inter-regional variability of the decrease in the highest values ([. 12 ]95 th percentile).
Furthermore, both observed trends and climatologies (i. e. time-averaged data) are used as reference values in order to evaluate the ability of models to reproduce the past atmospheric composition, and thus to forecast future changes. Consequently, assessing the mean distribution and the long-term evolution of [. 25 ]O 3 and CO is within the focus of several research programs dealing with the understanding of atmospheric composition, regarding air quality and climate issues. The present study is based on the measurements of these two trace gases, available in one single data set, the IAGOS database (http://www. iagos. org). In the framework of the IAGOS program (In-service Aircraft for a Global Observing System;  Petzold et al. , 2015) , the measurements on board commercial aircraft at [. 26 ]cruise levels (9-12 km above sea-level) provide an accurate sampling with a high frequency in the UTLS, particularly over northern mid-latitudes.
42 ]tracer-tracer correlations, and highlighting further the transport and mixing processes on a wider vertical range [. 43 ]. However, the IAGOS program using commercial aircraft as a multi-species measurement platform [. 44 ]enables a better characterization of inter-regional differences, given its higher spatio-temporal resolution. One of the objectives of the present study is to emphasize the use of IAGOS as a complementary database to the above-mentioned ones, allowing further investigations on composition and trends in O 3 and CO in the UTLS ([. 45 ]8-12 km) in the [. 46 ]Northern Hemisphere. [. 47 ]Concerning carbon monoxide, previous studies using satellite measurements have shown a significant decadal decrease in the CO column at the global scale, with a higher sensitivity in the mid-troposphere.
70 ]to derive trends, and a more complete data set with the addition of CO [. 71 ]measurements. Today, the IAGOS database allows such an analysis using regular and high resolution measurements in the UTLS, spanning 19 years for O 3 and 12 years [. 72 ]for CO. This paper aims at providing the most complete picture of the [. 73 ]O 3 and CO [. 74 ]distributions at a hemispheric scale based on in situ measurements. We investigate regional differences in terms of seasonal cycles and trends over the northern mid-latitudes. In Sect. 2 we describe the IAGOS program, the selected UTLS definition and the methodology used to [. 75 ]analyze the data. In Sect. 3 we present the climatologies, time series and analysis of the subsequent trends. 2 Data and methodology.
IAGOS data set The data used in this study [. 76 ]are provided by the IAGOS European research infrastructure (www. iagos. org; Petzold et al. , 2015). IAGOS consists in automatic measurements of several trace gases and aerosols on board commercial aircraft, aiming at a better understanding of the chemical composition of the atmosphere at the global scale. IAGOS has partly taken over the MOZAIC program (Measurement of [. 77 ]OZone and water vapour on Airbus In-service [. 78   ]airCraft;  Marenco et al. , 1998). The measurements of ozone and water vapour started in August 1994  (Thouret et al. , 1998; Helten et al. , 1998). The measurements of CO started in December 2001  (Nédélec et al. , 2003). Routine IAGOS-Core [. 79   ]measurements started in July 2011, with a new concept of aircraft system (e.
g. near real time data transmission) and instruments (e. g. combined ozone and CO in one single analyzer, as detailed in  Nédélec et al. , 2015). [. 80 ]Respectively, ozone and carbon monoxide have been monitored [. 81 ]with dual-beam UV and IR absorption photometers. Measurements are affected by an uncertainty range of [± 2 % ± 2 ppb] for ozone  (Thouret et al. , 1998)  and [± 5 % ± 5 ppb] for CO  (Nédélec et al. , 2003) , with a temporal resolution of 4 s and 30 s[. 82 ]. Although the technology remained the same, the consistency between MOZAIC and [. 83 ]IAGOS-Core data sets raised questions and the overlapping period ([. 84 ]2011-2014) has been used to give the users the assurance that [. 85 ]MOZAIC-IAGOS is a unique data set (named IAGOS database from now on). Indeed, it is of particular importance to make sure that the long-term evolutions of O 3 and CO distributions would not be biased by this technical change.
A deep and thorough comparison between MOZAIC and IAGOS, based on several hundred vertical profiles over Frankfurt, has been summarized in  Nédélec et al. (2015). The consistency between the two data sets has been demonstrated and hence, the results in the present study can be considered as independent of the transition from MOZAIC to IAGOS-Core. Thus, the recent study by  Petetin et al. (2016) , as well as the [. 86 ]Tropospheric Ozone Assessment Report  (Gaudel and Cooper, 2017, in preparation)   properties make it necessary to distinguish between upper troposphere and lower stratosphere. Thus, we discriminate the air masses with three varying pressure intervals, i. e. the upper troposphere (UT), the lower stratosphere (LS) and the tropopause layer (TPL) as a mixing region between. In extratropical latitudes, the discrimination between the [.