dataesr/scientific-paragraphs-categorization huggingface dataset:
Genes that pass multiple hypothesis testing are denoted with an asterisk (Benjamini-Hochberg, FDR < 0. 05) (*). (C) Pathway enrichment analysis results for 658 genes that are significantly differentially expressed (-1 > log 2 FC < 1, FDR < 0. 05) in PPD-B stimulated whole blood samples from both M. bovis AF2122/97-and M. tuberculosis H37Rv-infected animals at both 14 and 42 days post infection in comparison to unstimulated whole blood samples. The graph depicts the association of each pathway with the differentially expressed gene list based on the SIGORA successes metric (circle size) and the number of genes annotated within the pathway ("#genes in pathway") while the colour bar depicts the significance of the association (Bonferroni < 0. 05). (D) The relative expression (transcripts per million, TPM, "log(TPM +1)") and the relative change in expression (log 2 fold change ("Log 2 FC")).
Processing, normalization and statistical analysis of genome array data An initial visual analysis (including hierarchical and K-mean clustering) of the microarray data was completed using GeneSpring GX software (Agilent, Santa Clara, CA). Raw cell files from the Affymetrix GeneChip Chicken Genome Arrays were imported into GeneSpring software and unfiltered differentially-expressed genes (DEGs; FDR ≤ 0. 01) identified for a preliminary hierarchical and k-means cluster analysis (Fig. 1.
only). Next, open-source R (version 3. 3. 3) [20]  software packages, available from Bioconducter  [21] , were used for data processing and statistical analysis of the single-channel microarray data (raw cell files) obtained in this study. The software package affylmGUI (version 1. 48. 0) was used to perform normalization and to determine differential expression  [22]. The robust multi-array average (RMA) method  [23]  was applied for background correction, quantile normalization and output of gene expression values on the log2 scale. Internally, affylmGUI uses the Linear Model Fit of LIMMA package (version 3. 30. 13) to assess differential expression of genes  [24]. Statistical significance was adjusted for multiple testing by controlling the Benjamini and Hochberg's false discovery rate  [25] , which was set at FDR ≤ 0.
05. The Affymetrix chicken annotation data package  [26]  was then used to annotate the differentially-expressed genes (DEGs). LIMMA was used to make pairwise comparisons (contrasts) across embryo and hatchling ages and to filter DEGs by the FDR adjusted p-value (P ≤ 0. 05) and a log2 ratio ± 0. 75 cutoff. This gene expression level threshold of log2 ratio ± 0. 75 (or a 1. 68-fold difference) has proven useful for statistical analysis of microarray data in model plants  [27].
Verification of differential gene expression by quantitative RT-PCR analysis A second aliquot of each total RNA sample from the 24 livers (above) was used for quantitative real-time PCR (qRT-PCR) analysis. The expression of 15 DEGs identified by microarray analysis was verified by qRT-PCR analysis (see Additional file 2). Selection of these candidate genes was based on their known function in pathways of interest or involvement in lipid metabolism. In additional, a panel of three invariant genes (COX7A2L, PCF11 and PRL14) was included in the qRT-PCR analysis for normalization of qRT-PCR expression levels. Firststrand cDNA was synthesized using 1 μg of total RNA, oligo-dT, random hexamer primers and SuperScript III reverse transcriptase kit (Invitrogen Life Technologies, Carlsbad CA). Primers used for qPCR were designed using Primer Express (Applied Biosystems, Foster City, CA).
The qRT-PCR assay was performed for each sample in duplicate wells using Power SYBR green PCR master mix (Applied Biosystems, Foster City, CA) and a gene-specific primer-pair (Sigma-Genosys, Woodlands, TX) on an ABI Prism Sequence Detection System 7900HT. Biogazelle qbase+ software  [29]  was used for pre-processing raw cycle threshold (Ct) data and the geNorm procedure for normalization of relative gene expression levels. The expression stability of candidate genes and a panel of invariant "housekeeping" genes was based on the geNorm (M) and coefficient of variation (CV) values across duplicate measurements of the 24 liver RNA samples. Ribosomal protein L14 (RPL14) and cytochrome c oxidase subunit VIIa polypeptide 2-like (COX7A2L) were selected as optimal co-reference genes with a mean of M = 0.
However, the Ingenuity Upstream Regulator Analysis predicts that lipid synthesis would be inhibited in embryos as indicated by the blue label and blue dashed lines. This IPA prediction is based on actual E/H expression ratios and the observation that the number of AR-DEGs with higher expression in hatchlings (denominator) is 2-times greater than that of embryos (numerator). A small group of 14 transcription factors controls a much larger group of downstream metabolic genes. The largest number of AR-DEGs was found in "Cytoplasm"; these genes encode metabolic enzymes, kinases, phosphatases and transporters. The "Plasma Membrane" is mainly composed of genes encoding transmembrane and G-protein coupled receptors, transporters, enzymes, kinases and phosphatases. The extracellular AR-DEGs are a mixture of growth factors, adipokines, cytokines and transporters embryo (i.
E16 vs. E18 contrast The E16 vs E18 contrast yielded the smallest number (231) of AR-DEGs (Fig. 2 ; Additional file 3), which prevented generation of highly-populated gene interaction networks by IPA. Interestingly, the most abundant DEGs expressed in the liver of E16 embryos when compared to E18 embryos were seven transcripts that correspond to feather keratin (FKER). Unfortunately, these avian-specific transcripts were rejected by Ingenuity software and not included in the IPA functional analysis of the E16 vs. E18 contrast.
E16 vs. D3 contrast The E16 vs. D3 contrast identified 1784 AR-DEGs with 852 transcripts up-regulated in liver of E16 embryos compared with 932 AR-DEGs in D3 hatchlings. DIO3 and MOGAT1 were also among the highest DEGs in E16 embryos from the E16 vs. D3 contrast whereas DIO2 and THRSPA were overexpressed DEGs found in liver of D3 hatchlings. Ingenuity Pathway Analysis (IPA) analysis of 814 "Analysis Ready" (AR) and differentially expressed (DE) genes (AR-DEGs) from the E16 vs. E20 contrast. The top 10 up-regulated genes in E16 liver have positive log2 ratios (E16/E20), whereas the 10 down-regulated genes possess negative log2 ratios (E16/E20), which indicates higher expression in liver of E20 embryos.
qRT-PCR analysis and verification of differential expression of 15 candidate DEGs Hepatic expression patterns of seven DEGs that were expressed higher in embryos and an invariant gene (COX7A2L) during the embryo-to-hatchling transition were examined by qRT-PCR analysis (Fig. 13 ). Six genes (MOGAT1, PDK4, FZD2, LDHB, DIO3 and ADIPOQ) show higher log-based expression in embryos and a sharp decline after hatching. Surprisingly, hepatic expression of DIO1 was relatively stable, except in E18 embryos where DIO1 was slightly elevated. The invariant gene (COX7A2L) was used by geNorm software for normalization of the qRT-PCR expression levels. The expression patterns of eight lipogenic genes during the embryo-to-hatching transition are depicted in Fig. 14. The hepatic expression of THRSPA and SCD was logscale and increased exponentially, reaching a plateau in liver of fed hatchlings between D3 and D9.
Additional support for the ectopic expression of hepatic FKER transcripts in E16 Leghorn embryos is provided by our initial observation of differential expression of a cDNA target (pgf1n. pk001. j5; GenBank BI064513) on a low-density 3. 2 K chicken microarray (NCBI GEO Platform GPL1742)  [19]. This cDNA clone (pgf1n. pk001. j5) was sequenced from a normalized adipose cDNA library derived from embryo and hatchling chickens. Furthermore, our recent transcriptional study of liver from rd/rd Leghorn embryos, riboflavin-deficient and riboflavin rescued, utilized the Arizona 20. 7 K chicken oligo array (NCBI GEO Platform GPL6049), where three 70-mer oligo targets [Roslin Institute Gallus gallus (RIGG) oligo; RIGG10897, RIGG14163 and RIGG14953] had 19-times greater abundance in liver of riboflavin-rescued embryos than riboflavin-deficient embryos between E13 and E15.
Thus, three independent chicken microarray platforms and multiple expressedsequence tag (EST) sequences in GenBank clearly support abundant "ectopic" expression of FKER in liver of E15 and E16 embryos from both egg-type (Leghorn)  [35]  and, presently, meat-type (broiler) chicken breeds. However, the biological function of FKER in the lipid-laden liver of late embryos and the relationship between hepatic expression and cutaneous expression of FKER in embryos will require further definitive study.
Conclusions The present analysis of the embryo-to-hatchling transition in meat-type chickens provides the first detailed view of the choreography of innate and dynamic transcriptional responses made by embryos and newly hatched chicks Six of these FKER transcripts were the highest DEGs found in liver of E16 vs. E18 embryos. GenBank Accession number BI064513 is our chicken abdominal fat cDNA clone pgf1n. pk001. j5, which was sequenced from a normalized chicken abdominal fat cDNA library Abbreviations: feather keratin (FKER), fold-change (FC), Gallus gallus (GGA) chromosome, GenBank accession number (ACC. NUM. ) during the critical metabolic jump from lipid-laden ectotherm to free-living self-sufficient endotherm, respectively. The metabolism of late (E16-E20) embryos is dominated by β-oxidation of yolk lipids, glycolysis and gluconeogenesis, which utilizes the residual albumen and other proteins in allantoic fluid ingested by the embryo just prior to hatching.
Availability of data and materials The minimum information about a microarray experiment (MIAME)compliant microarray data set obtained from this study are available from the NCBI GEO database under accession number GSE109451 [https:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE109451]. Additional data generated or analyzed during this study are included within this published article and its supplementary files. Multiple worksheets in Additional file 3 provide lists of 14 pair-wise contrasts of DE gene sets embryos and hatchling chicks from statistical analysis of the microarray data.
All animals were used to represent the survival curves; n = 1 Remodelin-treated Lmna G609G/G609G mouse presented with haemangioma at 79 days of age and was censored into the survival analysis. Treatment and weight measurements were carried out by the mouse facility technicians blind of the scientific background or goals of the experiment. The comparison between Lmna G609G/G609G vehicle and Lmna G609G/G609G no treatment showed no significant difference (p = 0. 56). We utilized an informal method of randomizing within each batch to assign mice to treatment using mouse database ID. Mice were used from multiple cages and litters. Assessment of remodelin toxicity in vivo and dosing. Remodelin toxicity was assessed by the Crown Biosciences on 12 6-week-old C57BL/6N female mice, with a BW of 20 g on average (see Table  S2.
Animals showing obvious signs of severe distress and/or pain were humanely killed. Animal that had lost significant body mass (>20%, emaciated) were killed. Animals found to have other severe health problems, i. e. , prolonged diarrhea, persistent anorexia, lethargy or failure to respond to gentle stimuli, labored respiration, or that could not get to adequate food or water, etc. , were removed from the study and killed (n = 3; subcutaneous administration; data not shown). LC/MS/MS method development was used to assess the test compound in plasma. A minimum of 6 standards with LLOQ < 5 ng/mL and a minimum of five standards back were calculated to within ±20% of their nominal concentrations. A total of six QC samples at three concentrations (Low, Mid, and High) were included in sample runs with a minimum of four QC back were calculated to within ±20% of their nominal concentrations.
Sections of heart containing aortic outflow, pulmonary artery and myocardium were stained using a primary antibody against alpha smooth muscle actin (SMA-Sigma Aldrich Cat No: A2547 1:1000) and a secondary antibody (Alexa 488 Life Technologies at 1:100). The heart sections were also stained using DAPI alone and with an antibody against Acetyl α Tubulin (K40) (5335 Cell Signaling). Representative histology images were obtained from whole slide images scanned on a Hamamatsu NanoZoomer in brightfield and fluorescence modes. The thickness of the subcutaneous fat layer in the skin and nuclei number in the aorta were measured using whole slide images and the Hamamatsu NDP view software at a magnification of ×200 (resolution of 0. 45 μm per pixel). The skin (always the same region/the flanks) and aorta regions for analysis were identified by a pathologist (MJ) and manually annotated using the HALO image analysis software (Indica Labs).
The cells inside these annotated regions were identified and counted using the HALO software CytoNuclear v1. 5 algorithm, the output of cell density (cells/mm 2 ) was used to differentiate between the treatment groups. SMA intensity quantification was performed using ImageJ. Oocyte culture and immunofluorescence. Oocytes were isolated from ovaries of 8-week old C57BL/6 N female mice and cultured in M2 medium covered by mineral oil at 37 °C. Isolated oocytes were maintained in prophase arrest by addition of 250 µM dbcAMP (Sigma; D0627). To induce resumption of meiosis, oocytes were released into dbcAMP-free medium. For immunofluorescence, oocytes were fixed for 30 min at 37 °C in 100 mM HEPES (pH 7; titrated with KOH), 50 mM EGTA (pH 7; titrated with KOH), 2% formaldehyde (methanol free) and 0.
The data were obtained following the SOPs available at IMPReSS (www. mousephenotype. org/impress)  40. Factors predicted to affect the variables were standardized where possible. If this was not possible, measures were taken to reduce potential bias. For example, the impact of different people performing the test was minimized (known as the "minimized operator") as defined in the Mouse Experimental Design Ontology (MEDO)  44. The data captured with the MEDO ontology can be found at http://www. mousephenotype. org/about-impc/arriveguidelines. In addition, pre-established reasons were defined for QC failures (e. g. , insufficient sample, error with equipment during test) and detailed using IMPRESS. This provides standardized options and criteria as agreed by area experts as to when data can be discarded.
All discarded data is retained and tracked in a database to allow QC-failed data to be audited. Phenotyping data are collected at regular intervals on age, sex, and strain-matched wildtype (control) mice. In total, at least seven homozygote mice of each sex per knockout line were generated for phenotyping. If no homozygotes were obtained from ≥28 offspring from heterozygote intercrosses at P14, the line was declared homozygous lethal. Similarly, if less than 13% of the pups resulting from intercrossing were homozygous survive to P14, the line was judged as being homozygous subviable. In this event, heterozygote mice were examined in the phenotyping screen. The random allocation of mice to experimental group (wildtype vs. knockout) was driven by Mendelian Inheritance. Because of the high-throughput nature of the phenotyping screen, blinding the operators to the identity of knockout lines (both which line to be studied and the zygosity of the individual mouse) during phenotyping was not employed as the cage cards used to identify the mice includes genotype information.
Mice were then culled by cervical dislocation and heart removal, followed by removal of other organs for analysis. During this series of procedures, the experimenters collecting images and blood samples were not blinded from the genotypes of the mice. However, the analysis of the blood parameters was performed blind and uploaded onto a database. The kyphosis index (KY) was calculated as the ratio between a line drawn between the caudal margin of the last cervical vertebra to the caudal margin of the sixth lumbar vertebra and a line perpendicular to this from the dorsal edge of the vertebra at the point of greatest curvature  45. For the data collected on the back curvature, a multilevel regression model was performed using R (package:nlme version 3. 1). A model (Eq. 1), treating genotype, sex and age as fixed effects whilst the repeat measure nature of the dataset was accounted for by treating each mouse as a random effect, was fitted to the data.
g. , fight wound leading to overt clinical presentation). Nat10 +/-and Lmna G609G high-throughput phenotyping data. Knockout data collected across multiple batches were compared to a year's worth of control data collected on mice from the same genetic background. For the continuous data, an iterative top down mixed modeling strategy fitting Eq. 1 was performed using PhenStat  46  , an R package version 2. 6. 0 47 freely available from Bioconductor  48. The package's mixed model framework was used as default except the argument equationType was set to withoutWeight and dataPointsThreshold was set to 2. The model optimization implemented will adjust for unequal variances. The genotype contribution test p value was adjusted for multiple testing to control the false discovery rate to 5%. This statistical method has been studied through simulations and resampling studies  49  and found to be robust and reliable with a multi-batch workflow, where the knockout mice are split into multiple phenotyping batches.
Y $ Genotype þ Sex þ Genotype Ã Sexþ 1jBatch ð Þ ð 2Þ For the categorical data, a Fisher Exact Test was fitted comparing the proportions seen between wildtype and knockout mice for each sex independently using PhenStat FE Framework with the default settings. This simple method is appropriate for categorical phenotyping data as discussed in Karp et al. 46. The minimum p value returned from the two tests for a variable was adjusted for multiple testing to control the false discovery rate to 5%. The number of caudal vertebrae were recoded to a categorical variable by classifying mice with less than 28 vertebrae as "low," those with greater than 29 as "high" and all others as "normal. " For ABR data, the knockout dataset was smaller with only four data points for each variable, therefore to meet the assumption of the test, the data was analyzed using a reference range plus methodology which calls a significant phenotype when the majority of animals lie outside the natural variation seen in the control animals  47.
The implementation within PhenStat RR framework is based on classifying the analyzable variable values as high, normal or low based on the natural variation seen within the control data and comparing the proportions seen with a Fisher Exact Test. The minimum p value returned from the two tests ((1) increase in high classification and (2) increase in low classification) for a variable was adjusted for multiple testing to control the false discovery rate to 5%. As a high throughput program with many variables and multiple analysis tools, a single power calculation would not help; instead, the pipeline has been developed through empirically selecting a workflow which has historically given hits at a rate that would be cost effective for the program. Data analysis for genotype comparison. Mixed model data analysis was performed using R (package: nlme version 3.
05. The difficulty associated with the breeding and viability challenged the production of these mice for the array of phenotyping used in this paper. 136 mating pairs were set up over more than 3 years that generated 181 Lmna G609G homozygous (single or Nat10 double-mutant) mice that were used at specific ages together with littermate controls. The n was thus limited by breeding constraint and we have used n > 5. Y $ Genotype þ Age þ 1jBatch ð Þ ð 3Þ Heart rate comparison. Heart measurements were performed using the ecgTUN-NEL (emka TECHNOLOGIES), a noninvasive ECG system, using the manufacturer's recommendations  51. All ECG recording sessions were performed during daytime and the data analyzed using the iox2, data acquisition, and analysis software (emka TECHNOLOGIES). Each animal was put inside the tunnel, which was then closed, ensuring the animal was properly restrained.
To minimize the effects of stress, animals were allowed to stay in the restraining system for 1 min before starting ECG recordings. Indeed, direct observation of the animals and ECG traces proved that they were calm and that the heart rate was stable. For data acquisition, a series of repeated measurements were done on the same animal at each time-point and data for the same animal was collected over the different week intervals. For the data collected on the heart rate screen, a multilevel regression model was performed using R (package: nlme version 3. 1). A model [Eq. 1], treating genotype, sex, and age as fixed effects whilst the repeat measure nature of the dataset was accounted for by treating each mouse as a random effect, was fitted to the data. The genotype effect was tested and contrasts used to directly compare Lmna G609G/G609G and Lmna G609G/G609G Nat10 +/- mice if the genotype effect was significant.
For the hypothesis test of primary interest, the impact of genotype, p-values were adjusted to account for the multiple comparisons completed to control the false discovery rate to 0. 05. Visual inspection of the data was used to assess whether variance was equal and no outliers were present thus ensuring the assumptions of the model were met. RNA extraction and qPCR analysis. RNA was extracted from tissues from n > 5 independent mice/group using the RNeasy fibrous tissue mini kit (50; cat. No. 7404; Qiagen) and quantified using the NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific). 2 µg RNA/sample was used to produce cDNA using the High-Capacity RNA-to-cDNA kit (cat. No. 4387406; Applied Biosystems/Thermo Fisher Scientific). qPCR was carried out using the TaqMan system (Universal Master Mix II, with UNG, 4440038; Applied Biosystems/Thermo Fisher Scientific) on an Applied Biosystems Quant Studio 3 machine.
n ≥ 3 mice were used for each genotype, with 50 ng cDNA for each sample run in triplicate or quadruplicate. Thermo Fisher Scientific Nat10 (Mm00462302_m1) and Cdkn1a/p21 (Mm00432448_m1) primers were used as experimental probes while Gapdh (Mm99999915_g1), Actb (Mm00607939_s1) and/or Rn18s (Mm03928990_g1) were used as endogenous controls. The Relative Standard Curve pre-set program was used throughout and data analysis was performed using the Relative Quantification application, powered by the Thermo Fisher Scientific cloud platform. To account for technical replicates the mean value for a mouse was calculated and used for the statistical assessment. For each sample, data was normalized to the endogenous controls and represented as relative to the wildtype control. Data was evaluated by visual inspection and an F test of the variance was calculated in order to estimate normality and equal variance.
All graphs and part of the statistical analysis in the manuscript (Student's ttests; Kaplan-Meier estimator and statistical analysis; Fishers exact test) were generated and calculated using GraphPad Prism version 7. 0a for Mac OS X, GraphPad Software, La Jolla, California, USA, www. graphpad. com. RNAseq data analysis. RNA was extracted as described above and quality control assessed using the 2100 Bioanalyzer (Agilent Technologies). Because of financial constrains we used n = 2 mice/group. Transcriptome data was obtained using paired end sequencing, with read lengths of 150 bp, on a NextSeq 500 machine. Trimmed reads were aligned using STAR aligner (version 2. 4. 2a) to the mouse genome assembly GRCm38. Normalization of the read counts and differential expression analysis was performed using three commonly used software programs: DeSeq2 52 , edgeR  53  , and Cuffdiff 2  54.