dataesr/scientific-paragraphs-categorization huggingface dataset:
Flow cytometric peripheral blood lymphocyte analysis Cell staining was performed on whole blood samples using a direct immunofluorescence method with erythrocytes lysis and washing, then lymphocyte subsets were identified using a 3-laser, 8color BD FACSCanto-II TM flow cytometer and FACSDiva software (BD Biosciences, San José, CA). The absolute numbers of subsets were calculated by multiplying their percentage by the total lymphocyte number obtained from an ABX MICROS 60 device (HORIBA ABX SAS, Montpellier, France). CD3 + HLA-DR + evaluated activated T-cells. The second combination discriminated naive (CD45RA + CCR7 + ), central memory (CD45RA -CCR7 + ), and effector memory (CD45RA +/- CCR7 -) CD4 + and CD8 + T-cell subsets  [26]. The last combination distinguished naive (CD19 + IgD + CD27 -), natural memory (CD19 + IgD + CD27 + ), post-germinal center (CD19 + IgD - CD27 + ) B-cell subsets  [27] , and plasma cell precursors (CD19 + IgD -CD27 high CD38 high ).
Statistical analyses Baseline characteristics were compared by Wilcoxon rank sum, χ 2 , or Fisher's exact test as appropriate. eGFR slopes were calculated using a single slope linear model for each patient who had at least three eGFR values. eGFR slopes before vs. after belatacept initiation were compared using a paired t test, as appropriate. Statistical analyses were performed with GraphPad Prism 5. 0 software using paired Wilcoxon rank tests to compare parameters with baseline values. Twosided significance tests were used throughout. P < 0. 05 was considered statistically significant.
This result is supported by experimental evidence indicating that costimulatory blockade inhibits T follicular  [32, 33, 35]. Consistently, our data indicate a significant reduction in circulating CD4 + and CD8 + effector T cells and post-germinal center and memory B cells after conversion to belatacept. Albeit small, these significant changes in peripheral cell subsets may actually have a biological effect, as previously described by others in other kidney transplant recipient cohorts  [36, 37]. Belatacept conversion showed clinical benefit. Patients' kidney function did not deteriorate and, although not statistically significant, the rate of GFR decline was reduced after conversion to belatacept. CNI interruption also resulted in a significant lowering of HbA1c in diabetic patients despite no change in sugar-lowering drugs.
This may infer that tacrolimus, which is a wellknown diabetogenic drug, may enable hyperglycemia late after its introduction. [38]  Importantly, conversion to belatacept improved blood pressure and metabolic profile and stabilized graft function. The patient on azathioprine had numerous recurrences of squamous cell carcinomas and has been free from recurrence since conversion to belatacept. One of the two patients with CNI-induced neuropathy reported significant improvement of the symptoms. Following conversion, all patients reported satisfaction with the new intravenous regimen and none asked to return to CNI. Our data also support the favorable safety profile of belatacept. Belatacept infusions were well tolerated, the adverse events were limited in number, and in no case were any adverse events attributable to belatacept.
All included patients were EBV positive, which reduced the risk for post-transplant lymphoproliferative disorder (PTLD) previously associated with belatacept therapy  [39]. In the present study, urinary CXCL9 levels were within the normal range in most of the measurements. The three patients with elevated CXCL9 levels were the only ones who eventually developed kidney dysfunction, possibly mediated by an immunological reaction. Because the CXCL9 measurements were done post hoc, this information did not bias the clinical actions of the treating physicians, thus explaining why patients did not receive a kidney-graft biopsy despite the high CXCL9 levels. However, these data provide rationale for future interventional studies testing the hypothesis that serial measurements of urinary CXCL9 can lead to early identification of patients at risk for acute rejection, for whom CNI tapering should be avoided or performed cautiously to improve safety.
Herein, urinary CXCL9 was measured by ELISA. The 12-to 24-hour requisite turnaround time for ELISAs is short but not ideal for "real-time" implementation of therapeutic changes based on assay results. However, urinary CXCL9 can be also detected by biolayer interferometry  [44] , an assay that requires less than 1 hour, making it potentially implementable in clinical transplantation practice. Our study has limitations, including the single-center, retrospective design. However, the fact that these data were generated in the "real-life" setting of a large transplant center underlines the potential of using this strategy of conversion together with urinary CXCL9 to minimize the risk of rejection and early treat the subjects that develop such complication. Lack of a control group is another limitation.
However, with the limitation of juxtapositions across different studies, our results compare well with previous conversion trials from CNI to belatacept in terms of acute rejection rate and metabolic improvement  [13, 15, 28, 29]. Overall, our data support the use of our strategy of conversion of kidney transplant recipients from CNI to belatacept and provide the rationale for a randomized control trial testing the hypothesis that CXCL9 driven management of patients improves the safety/efficacy profile of this procedure over standard serum creatinine monitoring. Supplemental Figure  1. 12-month follow-up eGFR according to entry level eGFR.
Meanwhile, ZBTB28 promoter methylation was detected in almost all downregulated or silenced cell lines by MSP analysis (Figure  1B ). The specificity of methylated primers was confirmed in not-bisulfite DNA (Figure  S1B ). Treatment with DNA methyltransferase inhibitor 5-aza-2'-deoxycytidine (Aza) and histone deacetylase inhibitor Trichostatin A (TSA) led to the restoration of ZBTB28 expression, reduction of its methylation level, accompanied by an increase in unmethylated alleles (Figure  1C ). Mutation analysis using public database showed that BCL6 is mostly amplified, whereas deletion or mutation of ZBTB28 was detected in lung, head and neck squamous (HNSCC), ESCC and gastric carcinomas (Figure  S2 ). These results suggest that ZBTB28 silencing in carcinoma cells is mainly mediated by aberrant CpG methylation.
We further analyzed ZBTB28 expression and methylation in primary tumors from Cancer Genome Atlas (TCGA) and GENT (Gene Expression across Normal and Tumor tissue) databases. Data showed significantly reduced ZBTB28 expression and increased methylation in lung, HNSCC and esophageal cancer tissues, compared to their corresponding normal tissues (Figure  S3A-B ). Increased methylation of ZBTB28 was significantly correlated with reduced ZBTB28 expression in lung cancer specimens from TCGA datasets (Figure  S3C ). Furthermore, ZBTB28 promoter methylation was detected in 87. 5% (84/96) Lung, 100% (21/21) NPC, 67. 5% (27/40) ESCC, 78. 9% (30/38) GsCa, and 91. 9% (34/37) colon primary tumors, but rarely in normal lung, nasal, esophagus, colon or gastric tissues (Figure  1D , Figure  S1C ,Figure  S4 ).
ZBTB28 anti-tumor ability was further examined in two (A549 and HT29) xenograft tumor models. The mean weight and volume of tumors were significantly lighter and smaller in mice that received ZBTB28-transfected cells compared to those that received cells transfected with a control vector (Figure  2G , Figure  S5G ). Immunohistochemistry (IHC) analyses showed that PCNA expression and the proliferation index in nude mice tissues were significantly decreased (Figure  S6A-B ). Collectively, these data indicate that ZBTB28 is able to significantly impede tumor growth, supporting the role of ZBTB28 as a functional tumor suppressor in human cancers.
ZBTB28 and BCL6 inhibit each other's transcription Protein-protein association networks predicted an interaction between ZBTB28 and BCL6 (Figure  S8A-B ). Our RT-PCR analysis showed that BCL6 was expressed in carcinoma cell lines with silenced ZBTB28 (Figure  4A ). Meanwhile, data from TCGA online database confirmed an inverse relationship between ZBTB28 and BCL6 expression in lung and ESCC carcinomas (Figure  4B ). Immunofluorescence assay demonstrated that ZBTB28 was co-localized with BCL6 in nuclei of carcinoma cells (Figure  4C ). In addition, Co-IP results showed that ZBTB28 could form a complex with BCL6 (Figure  4D ). Therefore, ZBTB28 and BCL6 may co-regulate gene expression in heterodimer form. We then tested whether there is any inverse functional correlation between ZBTB28 and BCL6 expression using constructed luciferase reporters containing BCL6 or ZBTB28 promoters.
ZBTB28 directly promotes p53 transcription In view of the highly similar binding domain of BCL6 and ZBTB28 (Figure  S8C ), we speculated that ZBTB28 might also directly regulate p53 transcription. We firstly searched for putative BCL6 transcription factor-binding sites (TFBSs) in p53 promoter from Jaspar (http://jaspar. genereg. net/). Three TFBSs for BCL6 (from -1708--1695, -1475--1462 and -977--964) were identified in the p53 promoter. Data from the TCGA online database confirmed a positive relationship between ZBTB28 and p53 expression in tumor cells (Figure  5A ). qRT-PCR showed that ZBTB28 did increase the mRNA levels of p53, and also upregulated p21, p27 and caspases (downstream targets of p53) levels in carcinoma cells (Figure  5B ). Furthermore, knocking down BCL6 could decrease the expression of p53 and p53-targeted genes induced by ZBTB28 ectopic expression (Figure  5C , Figure  S8D ).
The primer sequences are listed in Table  3. The mean expression level of ZBTB28 in paired surgical margin tissues was considered as 1, and the relative expression levels of ZBTB28 in tissues were standardized to β-actin levels. An online cancer database (http://www. oncomine. org) was used to examine the mRNA expression of ZBTB28 in normal versus tumor tissues. P-values for each group were calculated using a Student's t-test. Standardized normalization techniques and statistical calculations are provided on the Oncomine website, and as previously described  [43].
Bioinformatics Sequences of cDNA were analyzed using the NCBI Blast program (http://www. ncbi. nlm. nih. gov/ BLAST/). Conserved regions were searched by multiple alignments of genomic sequences using ClustalW (http://www. ebi. ac. uk/Tools/clustalw2/ index. html). Potential transcription factor binding sites were identified with MatInspector (http://www. genomatix. de//matinspector. html) and the Jaspar database (Jaspar. genereg. net).
Acknowledgments This study was supported by  National Natural Science Foundation of China  (# 81872380 , # 81572769 , # 31420103915 , # 81772869 ),  Natural Science Foundation of Chongqing  ( cstc2018jscx-mszdX0039 ),  National Key Research and Development Program of China  (# 2017YFE0191700 ),  Hong Kong HMRF  (# 16151042 ) and RGC (GRF# 14115019 ).
3a, b ), while the other dataset shows enrichment in the post treatment samples (Fig. 3c ). Similarly, in vedolizumab-treated samples (Fig. 3d ), there is a clear mononuclear phagocytes enrichment in non-responders before treatment, and they are further highly enriched after treatment. In order to investigate whether nonresponders have more severe disease, we investigated the disease scores in pre-treatment samples for both infliximab and vedolizumab where data are available. There is no difference between responder vs. non-responder disease scores, which indicates that disease severity is not a factor that leads to treatment resistance. To evaluate the prediction performance of each cell type for treatment non-response, we used ROC to measure the true-positive rates against the false positive rates at various thresholds of prediction scores and calculated the AUC score.
DC and monocytes are enriched in diseased and treatment-resistant samples We measured the enrichment of different myeloid subsets in IBD colon biopsies using the adopted ten gene signatures representing 6 DCs (DC1 to DC6) and 4 monocytes (Mono1 to Mono4) from Villani et al. [28] , and assessed their enrichment in the datasets discussed above. For the DC subsets, conventional DC1, known for antigen cross presentation to CD8 T cells, was defined by CLEC9A instead of the classical surface marker CD141. DC2 and DC3 were both defined by the myeloid cell marker CD1C, but like DC1 shared a common conventional DC (cDC) progenitor. However, DC2 and DC3 differed from one another as DC3 can also originate from monocyte precursors (Mono1, CD14+ classical monocytes). DC4 was defined by absence of DC1/DC2/ DC3 phenotyping markers CD141 and CD1C while retaining the generic myeloid marker CD11C.
Discussion The current manuscript performed systematic in silico analyses to de-convolute 31 immune cell types, with a focus on MNPs, in colonic mucosal biopsies in multiple publicly available data sets, which include both CD and UC, as well as different locations of the inflamed gut. Besides comparing inflamed vs. non-inflamed tissues, indepth analyses were concentrated on treatment response mechanisms, for both infliximab and vedolizumab. It outlines an emerging picture -elevated mononuclear phagocyte subsets in inflamed colonic mucosal biopsies, and the pattern persisting only in non-responders after either infliximab or vedolizumab treatment. Further, we observed a correlated elevated expression of chemokine subset, which plays a role in myeloid cell function and contribute to generate and sustain inflammatory pathology.
More importantly, in addition to in silico analysis, we also evaluated the levels of several macrophage and DC subsets; and confirmed their elevated abundance in a chronic DSS colitis model. Gene signatures are gene sets that are coordinately regulated under specific biological conditions or across multiple biological states. Use of gene sets (See figure on previous page. ) Fig. 3  Cell type enrichment in response to biological treatment in IBD patient. a Cell type enriched in non-responder vs. responder to infliximab in UC (GSE16879). b Cell type enriched in non-responder vs. responder to infliximab in CD (GSE16879). c Cell type enriched in non-responder vs. responder to infliximab in UC (GSE73661). d Cell type enriched in non-responder vs. responder to vedolizumab in UC (GSE73661). NES: normalized enrichment score.
However, since macrophages are absent in blood, monocyte precursors of tissue macrophages probed in the context of IBD. Thus, the first set of analysis allowed us to generate data on M1 and M2 polarized macrophage gene signatures both in CD and UC samples, where the potentially pro-inflammatory and thus pathogenic M1 subset was enhanced. In contrast, the potentially anti-inflammatory and thus protective M2 subset was diminished with severity. In terms of DC subsets, the scRNA-seq data stratified DCs based on non-inflammatory healthy situation. Thus, the first set of analysis could additionally capture DC subsets which can be a byproduct of inflammation (iDCs) or activation (aDCs). Indeed, aDCs were found to be highly enriched with disease severity in UC and CD, and also with treatment non-responsiveness across the spectrum studied.
Muc2 deficiency represents a fundamental compromise in (See figure on previous page. ) Fig. 5  DC, monocyte subsets enrichment in response to biological treatment in IBD patient. a Cell type enriched in non-responder vs. responder to infliximab in UC (GSE16879). b Cell type enriched in non-responder vs. responder to infliximab in CD (GSE16879). c Cell type enriched in nonresponder vs. responder to infliximab in UC (GSE73661). d Cell type enriched in non-responder vs. responder to vedolizumab in UC (GSE73661). NES: normalized enrichment score. NR: non-responder; R: responder barrier function which might be common in both human and mice. In fact, Muc2 deficient mice have been shown to be similar to active human UC disease  [39]. pDCs were found to be exacerbated in intestinal mucosa and mesenteric lymph nodes of IBD patients.
Th17 cells or ILCs are reported to be enriched in inflamed colon and emerge as novel immune cell populations that play important roles in IBD pathogenesis  [44, 45]. Gene expression of IL17 and IL22, key cytokines produced by Th17 and/or ILCs and linked to IBD pathogenesis, were further examined in treatment response data sets (Additional file 1: Figure  S4 ). Both IL17A and IL22 are elevated significantly in inflamed tissues from CD or UC patients, before treatment. After treatment, both genes return to normal control level in responders, but not in non-responders. In addition, either IL17 or IL22 gene expression is higher in nonresponders vs. responders in pre-treatment samples from infliximab studies, which suggest that potentially Th17 or ILC could contribute to treatment resistance. Interestingly, similar pattern was not observed in the vedolizumab samples.
Transcriptomic data sets Three transcriptomic studies (GSE100833, GSE73661, GSE16879) for samples from both CD and UC patients, representing different locations of the gut, disease severity and treatment response (infliximab, vedolizumab), were downloaded from GEO database and used in the analysis. For each data set, we standardized the transcriptome data across patients by quantile-normalization. scRNA-seq data for stroma cells from UC patients were downloaded (GSE114374). Cells were QC, selected and normalized by using Seurat package (v2. 3) [52]. From GSE16879, two separate data sets represent infliximab treatment study in CD and UC. In CD patients, there are 12 responders vs. 7 non-responders. In UC patients, there are 8 responders vs. 16 nonresponders. From GSE73661, one study contains infliximab treatment in UC patients including 8 responders vs.
Cell type signature and enrichment analysis Immune cell type gene signatures were from xCell study which were compiled from over thousands of sorted human cell type transcriptomes from various resources  [25]. There are 165 gene signatures with number of gene > = 15, representing a total of 31 immune cell types which were included in the analysis (iDC and NKT gene signatures were removed due to small number of genes). DC, monocyte subset gene signatures are from scRNA-seq study of human blood DC, monocytes  [28]. Two sample population GSEA analysis  [26]  was performed for each gene signature. Normalized enrichment score and p values were averaged for the same cell type. Single sample enrichment analysis were based on the analysis of xCell  [25]. Correlation analysis of DC, monocyte subsets and proinflammatory signature Spearman correlation analysis was performed for each pair of DC, monocyte subset (after removing common genes) for their expressions in either UC or CD transcriptomic data using R v3.
5. 1. The heatmaps were generated using gplots v3. 0. 1. 1 (https://cran. r-project. org/web/packages/gplots/index. html) with default parameters. Similarly, their potential function in inflammation was also assessed by evaluating their correlation with a proinflammatory gene signature. The expression value of each signature was represented by the median value of all member in the specific data set.
Flow Cytometry Single cell suspensions from IEL and LP were labelled with a cocktail of flurochrome conjugated antibodies against surface antigens purchased from either BD Biosciences (San Jose, CA) or Biolegend (San Diego, CA): anti-CD45 FITC, anti-CD19 PerCP Cy5. 5, anti-Siglec H Pacific Blue, anti-I/A I/E BV605, anti-CD11b BV 650, anti-CD3e BV786, anti-B220 BUV496, anti-EpCam APC, anti-F4/80 AF700, anti-Ly6c APC-Cy7, anti-CD11c PE-CF594, anti-CD103 PE-Cy7 and Zombie aqua kit. Cells were fixed with 1% PFA prior to acquisition in Flow Cytometry. Cells were acquired using a 18 color BD LSRFortessa (San Jose, CA) and analyzed with FlowJO software (Ashland, Oregon).
Study design. Ascent profiles, details on logistics as well as barometric pressure (P B ), inspired partial pressure of oxygen (P i O 2 ) and temperature at the respective altitude laboratories have been reported previously  32. Baseline measurements were performed in London (LDN) in a laboratory at UCL (75 m above sea level, PiO 2 19. 7 KPa) between January 4 th and February 26 th 2007. Field studies were performed between 31 st March and 6 th June 2007 at laboratories set up at the following locations (altitudes expressed as meters, m): Kathmandu (KTM, 1,300 m, PiO 2 16. 8 KPa), Namche (NAM, 3,500 m, PiO 2 12. 7 KPa), Pheriche (PHE, 4,250 m, PiO 2 11. 6 KPa) and Everest Base Camp (EBC, 5,300 m, PiO 2 9. 9 KPa). Data collection, including plasma sample collection, was performed at each of these time points.
D 2 O was required for the magnetic lock frequency and DSS was used as a chemical shift reference. The resulting solution was transferred to 5 mm NMR tubes within a 96-tube rack. Proton ( 1 H-) NMR spectral acquisition and processing. 1  H-NMR spectra of plasma samples were obtained using a Bruker Avance III 700 MHz spectrometer (Bruker Biospin, Karlsruhe, Germany) as described previously  34. All samples were analysed on a participant by participant basis, meaning that plasma taken from the same participant at all 5 time points was defrosted and treated at the same time and run within the same NMR experiment. This was to ensure any daily differences in experimental procedure were removed from analysis when data between time points were compared. Resulting spectra were processed using TopSpin (Bruker, Karlsruhe, Germany).
Spectra were converted from time to frequency domain using a Fourier transform. The phase was adjusted and spectra aligned so that the DSS peak corresponded to 0 ppm through use of the icoshift program in Matlab  35. Due to the degree of noise present, the aromatic region was excluded and only the aliphatic spectral region analysed. Normalisation and scaling of this spectral region were performed through use of Probabilistic Quotient normalisation  36  and Pareto scaling  37  , respectively (Supplementary Fig. 1 ). Initial principal components analysis (PCA) on this region revealed a separation into two regions that was not due to variation dependent on the experiment i. e. a change with location and thus with altitude (Supplementary Fig. 2 ). Instead, it was likely due to sample acquisition differences such as slight alterations in gain between NMR batch runs, as the samples were run in two batches at different times.
A bias trend was identified to capture 77% of this variance (Supplementary Fig. 3 ) and was subsequently normalised using EigenMS (Supplementary Fig. 4 )  [38] [39] [40]. As all samples from any single subject were run at the same time, they were contained within one of the two batch effect groups. Following this, the full resolution spectra were binned at a ratio of 10:1. This ratio was found to sufficiently reduce the size of the data set, whilst retaining maximal information. The total number of bins was 1203. Putative identification of the metabolites associated with the peaks undergoing significant changes was undertaken using Chenomx software (Chenomx NMR Suite 7. 1).
Data modelling using Bayesian Statistical Methods. Model justification. The data recorded in the present study were naturally hierarchical: there were multiple measurements of each metabolite in each subject; yet for the present experiment, the aim was to identify those metabolites that were consistently affected by altitude across the entire study population. For this reason, we adopted a hierarchical approach to statistical modellingspecifically we fitted a hierarchical Bayesian model for each metabolite. This model comprised robust regression models (robust in that the residuals were modelled with a t-distribution  46  ) for each binned 1 H-NMR region or lipid variable at the participant level; the parameters from these regression analyses (specifically the slope and intercept, β1 and β0) were themselves modelled with normal distributions.