dataesr/scientific-paragraphs-categorization huggingface dataset:
These results imply that in younger seedlings SMAX1 and SMXL2, thus the KAI2 pathway, might play a more pronounced role than that of D14, as previously suggested (Villaécija-Aguilar et al. 2019). Overall, both RNA-seq and qRT-PCR data indicate that in the roots only minor transcriptional changes occur in the SMAX1, SMXL2, and SMXL7 levels. Beyond the negative regulators of the SL/KAR signaling, our dataset also contained genes involved in the phenylpropanoid pathway for flavonoid/flavonol production that were downregulated in the max2 mutant, confirming our previously published proteome data  (Walton et al. 2016). The expression of CHS and FLS1 was reduced in all mutants, indicating that the endogenous root flavonol production at the seedling stage is controlled by both D14 and KAI2, in agreement with former data demonstrating that flavonol responses to exogenous rac-GR24 treatments are mediated by both receptors  (Walton et al.
RNA-seq analysis Approximately 100 seedlings, grown as mentioned above, were used for each treatment and the experiment was repeated three times. RNA-seq was done after RNA quality control and in-house library preparation for mRNA enrichment. The samples were run on an Illumina HiSeq 2000 platform with a paired-end sequencing mode and a read length of 50 base pairs. The raw data files contained 15-18 million reads per sample. First, these data files were quality checked and filtered by FastQ manipulation that removed all reads for which more than 25% of the bases in the sequence had a quality score below the Q20 threshold (1% of total reads). Next, the 3'-end were trimmed with the FastQ Qual trimmer that removed all ends with a quality score below Q10. Lastly, reads were paired forward and reverse and mapped on the Arabidopsis genome by means of the annotated genome of The Arabidopsis Information Resource (TAIR10).
Root phenotypes The roots of 9-day-old seedlings were counted under a S4E light microscope (Leica Microsystems), and the main root length was measured with the ImageJ software. The root hair length of 5-day-old seedlings were measured with the ImageJ software in a 1-mm segment of the primary root, in which hairs were fully elongated, approximately at the same distance from the root tip  (Fernandez et al. 2013). The numbers and overlaps of differentially expressed genes (Supplementary Datasets S1-S3) were determined and are shown as UpSet plot  (Lex et al. , 2014). The vertical bar chart gives the numbers of each genotype and condition, whereas the number of unique and overlapping genes is represented by horizontal bars, with the dot matrix indicating the respective overlaps by connected circles.
Acknowledgements We thank  Ralf Stracke  ( Bielefeld University, Bielefeld, Germany ) for providing the myb mutants and our colleagues  Bert De Rybel  for the tmo5/tmo5l1 double mutant,  Boris Parizot  for tips on the RNA-seq analysis,  Veronique Storme  for statistical help on both the RNA-seq and lateral root density, and  Martine De Cock  for help in preparing the manuscript.
Acknowledgments: The authors are grateful to all partners ( INRAE Dijon UMR Agroécologie, Muséum National d'Histoire Naturelle, Association Française d'Agroforesterie, CTIFL, Station de la Noix de Creysse ). The authors thank  Magali Maniez ,  Anne Cauchois  and  Maeva Leplet  (UniLaSalle,  AGHYLE Research Unit UP 2018. C101 ,  Rouen, France ) for physicochemical analyses.
Funding: This work was funded by  MycoAgra  research project entitled " Interests of MYCOrrhization in AGRicultural and Agroforestry practices " ( 2016-2020 )  AAP IP 2016 N • 5606_2102082721  and supported by  CASDAR "Compte d'Affectation Spéciale Développement Agricole et Rural" of the French Ministry of Agriculture and Food. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Oocyst Shedding The oocyst shedding was evaluated by collecting freshly excreted fecal pellets from each mouse at the time of euthanasia. Total genomic DNA was extracted from 200 mg of feces by using the QIAamp Fast DNA Stool Mini Kit (Qiagen GmbH, Hilden, Germany) according to the manufacturer's protocol. The presence of parasite was detected by using the TaqMan real time PCR (qPCR) assay as previously described  [25]. Briefly, the assay amplified a DNA fragment located in the 18S rRNA gene locus (GenBank accession no. EU675853, positions 33 to 211). The forward (5 -CATGGATAACCGTGGTAAT-3 ) and reverse (5 -TACCCTACCGTCTAAACTG-3 ) primers were designed to amplify a 178 bp fragment. A TaqMan probe homologous to a conserved region of the sequence (Pan-crypto probe; FAM-CTAGAGCTAATACATGCGAAAAAA-MGB-BHQ [FAM, 6-carboxy-fluorescein; MGB, minor-groove-binding ligand; BHQ, black hole quencher]) was designed to detect Cryptosporidium.
The qPCR reactions were performed on a Rotor-Gene 6000 instrument (Corbett Research, Qiagen) with 50 ng of extracted DNA, 200 nM of each primer, 100 nM of the probe and 1X of Light Cycler 480 Probes Master. The gene amplification consisted of activation of the Taq DNA polymerase for 10 min at 94 • C, followed by 45 cycles of 94 • C for 10 s, 54 • C for 30 s and 72 • C for 10 s. Fluorescence signal was acquired at end of elongation step and the amplification data were analyzed using Rotor-gene Q Series software. The results were displayed using GraphPad Prism (version 9. 1. 0 (221), latest update on 2021/03/15).
After purification using a RNeasy Mini Kit (Qiagen), cRNA yield and incorporation efficiency (specific activity) into the cRNA were determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). For each sample, a total of 600 ng of cRNA was fragmented and hybridized overnight at 65 • C. After hybridization, slides were washed before being scanned on a SureScan Microarray Scanner (Agilent Technologies) and further processed using Feature Extraction v10. 7. 3. 1 software. The resulting text files were uploaded into language R v4. 0. 3 and analyzed using the Linear Model for Microarray Data (LIMMA) package  [29, 30]. A 'within-array' normalization was performed using LOWESS (locally weighted linear regression) to correct for dye and spatial effects  [31]. Moderate t-statistic with empirical Bayes shrinkage of the standard errors  [32]  was then used to determine significantly modulated genes.
Data Analysis To visualize and explore the molecular interaction networks of the differentially expressed genes, the subsequent data were uploaded into the Ingenuity Pathways Analysis (IPA) software (QIAGEN CLC Genomics Workbench 20. 0; version 68752261; latest update on 6 September 2021) (https://digitalinsights. qiagen. com/) (Qiagen, Redwood City, CA, USA) to organize the differentially expressed genes into networks based on the Ingenuity Knowledge Database (IKB), an extensive database of functional direct and indirect interactions between genes from peer-reviewed publications. Particularly, the present study applied IPA system to uncover the signaling pathways, interactions and functional roles associated with differentially expressed genes in C. parvum infected caecum tissues in comparison with controls.
PCR amplification consisted of activation of the Taq DNA polymerase for 3 min at 95 • C, followed by 40 cycles of 95 • C for 10 s, 57 • C for 20 s and 72 • C for 20 s. Fluorescence signal was acquired at the end of elongation step and the amplification data were analyzed using Rotor-gene Q series software. The 2 -∆∆Ct method was used to calculate the relative expression levels of target genes in infected mice with the constitutively expressed endogenous control and the ∆C t value of the uninfected mice was used as the calibrator  [35]. The results were displayed using GraphPad Prism (version 9. 1. 0 (221), latest update on 15 March 2021).
Chronic Cryptosporidiosis Induced Neoplasia Results in Global Gene Profile Alterations The gene expression profiles were significantly altered with 43 and 931 genes reported to be differentially regulated at day 45 PI and day 93 PI, respectively (LogFC2. 0; adj p-value < 0. 05) as shown in Table  1. The entire lists of regulated genes are provided as Supplementary Tables  S2  and S3. Fold changes of infected mice at day 45 PI and day 93 PI were compared with those of control uninfected animals of matching genotype with significant adj p-value (<0. 05). The experimental datasets from each time point were subjected to core analysis using IPA software. Analysis was performed to characterize the global functions associated with the altered gene profiles. As a result, 37 genes at day 45 PI and 900 genes at day 93 PI were mapped using the software.
The software predicted 35 genes to be "analysis-ready molecules" at day 45 PI wherein 33 of them were predicted as upregulated and the two remaining ones as downregulated. At day 93 PI, 758 "analysis-ready molecules" were predicted wherein 566 and 192 genes were predicted to be upregulated and downregulated, respectively. As a part of core analysis, the experimental datasets were also subjected to different features of the software which included "upstream regulators", "diseases and functions" and "regulatory effects" analysis. The upstream regulators are the predicted molecules involved in the regulation of gene expression changes within the datasets. By applying the threshold of overlap p-value < 0. 05, a total of 41 and 306 upstream regulators were identified at day 45 PI and day 93 PI, respectively.
The overlap p-value measures whether a statistically significant overlap is pointed out between the dataset genes and the genes that are regulated by the transcriptional regulator. Among them, 29 (activated) had activation z-score > 2 and 12 (inhibited) had activation z-score < -2 at day 45 PI whereas about 140 upstream regulators were predicted to be activated and about 166 inhibited at day 93 PI. The top three upstream regulators identified at both time points were Lipopolysaccharide (LPS), Interferon-γ (IFNγ) and Tumor necrosis factor (TNF) (Figure  2A ). IFNγ was observed to be the most powerful activator at day 45 PI (Z-score = 3. 954, overlap p-value = 2. 97 × 10 -15 , 16 target molecules) whereas LPS was identified as the most powerful activator at day 93 PI (Z-score = 11. 165, overlap p-value = 3.
23 × 10 -93 , 277 target molecules). Further, by applying the -log (p-value) > 4 threshold, the top diseases and cellular functions associated with the molecules in the datasets were determined. As shown in Figure  2B , the top four "Diseases and Disorders" observed to be common at both time points were as follows: "Gastrointestinal Disease" Moreover, regulatory effects analysis algorithm was used to connect the upstream regulators, dataset molecules and downstream disease and functions to generate a hypothesis of how a particular function is regulated in the dataset. The regulator effects are determined in terms of a consistency score which is a measure of how casually consistent and densely connected a regulatory network is. The upstream regulators and diseases and functions included in the analysis were of -log (p-value) > 4 and z-score > 2.
At day 93 PI, the experimental dataset revealed downregulation of the chemokine CCL20, known to exert anti-microbial activity against C. parvum (Figure  3B ). The analysis also identified α-defensins 4 (DEFA4), an anti-microbial peptide, to be highly upregulated at day 45 PI (Log FC = 6. 37) compared to day 93 PI (Log FC = -6. 07) wherein it was highly downregulated (Figure  3B ). Other isoforms of α-defensins, DEFA1, DEFA2 and DEFA3 (α-defensins 1, 2 and 3, respectively) were also observed to be highly downregulated.
At day 93 PI, the experimental dataset revealed downregulation of the chemokine CCL20, known to exert anti-microbial activity against C. parvum (Figure  3B ). The analysis also identified α-defensins 4 (DEFA4), an anti-microbial peptide, to be highly upregulated at day 45 PI (Log FC = 6. 37) compared to day 93 PI (Log FC = -6. 07) wherein it was highly downregulated (Figure  3B ). Other isoforms of α-defensins, DEFA1, DEFA2 and DEFA3 (αdefensins 1, 2 and 3, respectively) were also observed to be highly downregulated. Using the tool "My Pathways" available in IPA, customized pathways were created for target molecules of interest including DEFA4, DEFA1, CIITA and IIGP1. Interferoninducible GTPase 1 (IIGP1) and class II, major histocompatibility complex, transactivator (CIITA) have been selected to showcase the network of genes known to be involved in inhibition of parasitic infection.
parvum to persist the infection. The network for each target molecule was built on the molecules and/or relationships available in the database exclusively from epithelial cells, small intestine and large intestine. Further, the developed pathways were overlaid with experimental datasets (Log FC2 adj p-value 0. 05) at day 45 PI and day 93 PI to measure the expression level of the molecules. As a result of this analysis, DEFA4 expression was observed to be indirectly regulated by intestinal epithelial insulin receptors (INSR). At day 45 PI, INSR is predicted to be inhibited which results in upregulation of DEFA4 in intestinal epithelial cells (Figure  4A ). However, at day 93 PI, we observe an increase in the expression of IGF1 (insulin growth factor 1) which is predicted to activate INSR, resulting in downregulation of DEFA4 expression.
Predicted activation of INSR is also supported by downregulation of other genes from the experimental dataset observed in the network such as hydroxyprostaglandin dehydrogenase (HPGD), pyruvate dehydrogenase (PDK4), PCK1 (phosphoenolpyruvate carboxykinase 1), aldehyde dehydrogenase family member A 1 (ALDH1A1) and aldoketoreductase family 1 member B7 (Akr1b7) (Figure  4B ). The second hypothetical network predicted DEFA1 expression to be regulated by TNF receptor associated factor 2 (TRAF2). At day 45 PI, DEFA1 was predicted to be downregulated with very low confidence score due to predicted inhibition of TRAF2 (Figure  4A ). However, at day 93 PI, with the increase in the expression levels of cytokines such as Interleukin 1β (IL1β) and TNF, DEFA1 was predicted to be downregulated by TRAF2 with a high confidence score (Figure  4B ).
Chronic Cryptosporidiosis Induces Tumor Microenvironment Experimental datasets from days 45 and 93 PI were subjected to comparative IPA analysis to identify enriched "Canonical Pathways" associated with cancer by applying the -log(p-value) > 2 threshold. As a result, cancer related canonical pathways were majorly identified in the dataset at day 93 PI compared to day 45 PI (Figure  5A ). "PD-L1 Cancer Immunotherapy Pathway" (p-value = 3. 02 × 10 -6 ) was the only pathway identified within the dataset at day 45 PI. However, this pathway was predicted to be inhibited with an activation z-score of -2. 0. Programmed cell death ligand-1 (PD-L1), also known as CD274, is an immune checkpoint molecule which is expressed by tumor cells or infiltrating myeloid cells to induce apoptosis of T cells and drive immune suppression which is highly expressed at day 45 PI (Log FC = 2.
099 at day 93 PI. This pathway had an overlap score of 15. 6% with 29/173 molecules found within the dataset. A heatmap was generated to identify the regulation of expression level of these overlapping genes. All the genes identified to activate the tumor microenvironment pathway were highly upregulated at day 93 PI except secreted phosphoprotein 1 (SPP1), Indoleamine 2,3-dioxygenase 1 (IDO1), UBD and CD274, which were also highly upregulated at day 45 PI (Figure  5B ). The other major pathways identified in terms of regulated gene numbers were HOX antisense intergenic RNA) regulatory pathway (HOTAIR) and HLA-F adjacent transcript 10, also known as UBD) cancer signaling pathway (FAT10). The identification of other genes from the datasets that are associated with canonical pathways such as "Glioma Invasiveness Signaling Pathway" and "Role of Tissue Factor in Cancer" highlighted the invasive nature of the tumor development.
This result was further confirmed by the identification of "Organismal Injury and Abnormalities" as one of the highly regulated disease/disorder (Figure  2B ). After applying a z-score > 2 as threshold, it was observed that most of the cellular functions that define this disease/disorder are associated with cancer. The heatmap generated for "Organismal Injury and Abnormalities", out of the 17 activated cellular functions with z-score > 2, 10 functions were associated with growth, metastasis, invasion and neoplasia of tumor. These 17 cellular functions are represented in terms of -log p values in Figure  5C. Invasive tumor (p-value = 9. 37 × 10 -54 ) had the highest activation z-score of 4. 013 with a total of 169 molecules involved in the dataset (Figure  5C ). Using the tool "My Pathways", customized pathways were created to target molecules of interest (TNF and STAT1).
The network for each target molecule was built on the molecules and/or relationships available in the databases exclusively from epithelial cells or cancerous epithelial cell lines. Further, the developed pathways were overlayed with experimental datasets (LogFC2 adj p-value 0. 05) obtained at day 93 PI to detect the expression levels of the predicted molecules. As shown in Figure  6A , TNF-α expression was observed to upregulate expression of several cancer-associated genes in the epithelial cells which include extracellular matrix (ECM) remodeling genes such as MMP9, PLAU, FN1 and ICAM1, chemokines such as CCL2, CXCL2, CXCL3 and CXCL10 and regulatory genes such as UBD and PTGS2. In IECs, expression of STAT1 gene was also predicted to be upregulated by inflammatory cytokines IL1β and IFNγ and growth factor FGF7 identified as key molecules involved in tumor microenvironment.
Discussion In the current study, a transcriptomic approach was conducted to explore the signaling pathways potentially involved in the development of neoplasia induced by C. parvum. Global upregulation of genes associated with the innate immune response was observed despite the administration to the animals with the anti-inflammatory and immunosuppressive drug, dexamethasone. The total number of genes subjected to core analysis by IPA was 37 genes for day 45 PI compared to 900 genes for day 93 PI. This difference in the number of differentially expressed genes per dataset could be attributed to the fact that it was possible to visualize the polypoid masses within the ileocecal region at day 93 PI before dissection for microarray analysis. In contrast, macroscopical lesions were not visible at day 45 PI and the samples were dissected blindly.
The core analysis of the datasets focused upon the following modules namely upstream regulators, disease and functions and regulator effects. The aim of IPA upstream regulator analytic is to identify the cascade of upstream transcriptional regulators that can explain the gene expression changes in the experimental datasets and in turn help to clarify the biological activities occurring in the tissue. The top three common upstream regulators (LPS, IFNγ and TNF-α) identified at day 45 PI and day 93 PI, are likely obvious consequences of the presence of C. parvum infection in the tissue. Indeed, the prevailing transcriptional response of IECs to C. parvum was to upregulate gene targets of IFNγ signaling  [36]. Moreover, TNF-α released from inflammatory monocytes assists C. parvum in loss of intestinal barrier to propagate the infection  [20].
In parallel, LPS from bacteria are known activators of Toll-like receptor 4 (TLR4) signaling which is similarly activated for C. parvum eradication after activation of the NF-κβ signaling pathway  [37]. Thus, identification of LPS as one of the upstream regulators suggested the presence of still unknown parasitic virulence factors homologous to LPS. The top "diseases and disorders" associated with the experimental datasets were "inflammatory response", "immunological disease" and "organismal injury and abnormalities". Moreover, identification of gastrointestinal disease as one of the disorders supports the fact that the differentially expressed genes were detected as a response to C. parvum. "Mammalian infection" was the top regulatory pathway identified to be inhibited at day 45 PI and the genes responsible for this action were associated with MHC class II molecules.
Hence, it can be suggested that intestinal epithelial cells express IIGP1 as an immune response against C. parvum but the parasite would be able to resist to this innate immune response. Then, the overexpression of IIGP1 would result in chronic inflammation contributing to development of cancer. The role of chronic cryptosporidiosis in the induction of digestive cancer was further confirmed after the analysis of molecular data at day 93 PI which allowed the identification of genes belonging to five canonical pathways associated with cancer. Moreover, the top disorder associated with this dataset termed as "Organismal Injuries and Abnormalities" was a result of several functions related to cancer progression such as neoplasia, growth, invasion, and metastasis of solid tumor. Identification of 29 out of 173 canonical molecules associated with "Tumor Microenvironment Pathway" predicts that C.
STAT1 and NF-κβ have been identified as key transcriptional regulators of PTGS2 expression, similar to what happens in H. pylori infection that induces gastric cancer via chronic inflammation that activates NF-κβ which in turn induces pro-inflammatory mediators such as IL1, IL6, IL8, TNF-α and PTGS2  [57]. UBD which also contributes to colon cancer progression  [58]  was observed to be upregulated in the datasets and its expression was predicted to be induced by TNF-α in transformed epithelial cells. Moreover, IECs infected with C. parvum showed upregulated expression of UBD along with interferon stimulated genes  [36]. Thus, it can be speculated that an inflammatory response induces the expression of UBD and its role in C. parvum associated oncogenesis can be explained by its ability to inhibit tumor suppressor p53.
[day 45 PI (p-value = 8. 06 × 10 -16 ), day 93 PI (p-value = 1. 90 × 10 -54 )], "Immunological Disease" [day 45 PI (p-value = 8. 04 × 10 -16 ), day 93 PI (p-value = 4. 11 × 10 -68 )], "Inflammatory Response" [day 45 PI (p-value = 1. 61 × 10 -21 ), day 93 PI (p-value = 8. 61 × 10 -64 )] followed by "Organismal Injury and Abnormalities" [day 45 PI (p-value = 1. 06 × 10 -23 ), day 93 PI (p-value = 8. 61 × 10 -64 )]. Out of the top four, "Organismal Injury and Abnormalities" was reported to have highest number of molecules to be involved from each dataset (day 45 PI = 30 molecules and day 93 PI = 582 molecules).
Background: Single nucleotide polymorphism (SNP) array technology has been increasingly used to generate large quantities of SNP data for use in genetic studies. As new arrays are developed to take advantage of new technology and of improved probe design using new genome sequence and panel data, a need to integrate data from different arrays and array platforms has arisen. This study was undertaken in view of our need for an integrated high-quality dataset of Illumina Infinium® 20 K and Affymetrix Axiom® 480 K SNP array data in apple (Malus × domestica). In this study, we qualify and quantify the compatibility of SNP calling, defined as SNP calls that are both accurate and concordant, across both arrays by two approaches. First, the concordance of SNP calls was evaluated using a set of 417 duplicate individuals genotyped on both arrays starting from a set of 10,295 robust SNPs on the Infinium array.