dataesr/scientific-paragraphs-categorization huggingface dataset:
6 he activation of innate and adaptive antiviral immunity by pomalidomide also enhanced ex vivo the expansion of cytotoxic NK cells and cell killing of autologous in vitro HIV-1 infected target cells. 7 Pemalidomide also significantly expanded HIV-1 specific CD8 + T cells and increased HIV-1 peptide-loaded target cell killing. In conclusion, some combinations of immunotherapies look promising in animal models and encouraging data is now emerging in human trials. It is suggested that bNAbs given at ART initiation may provide a sustained boost to HIV-1 specific T-cell function and improve ART-free control. We still do not know though if immunotherapeutic targeting Contents lists available at ScienceDirect Journal of Virus Eradication journal homepage: www. sciencedirect. com/journal/journal-of-virus-eradication https://doi.
Statistical Analysis All data were tested for normality distribution using the Shapiro-Wilk normality test. When data were normally distributed, homogeneity of variance was checked by Bartlett's test and paired t-tests were performed. When data were not normally distributed at all-time points, nonparametric paired tests (Friedman or Mann-Whitney tests) were used after appropriate corrections if needed. Ex aequo values were corrected according to Hollander and Wolfe. Statistical analyses were performed using XLSTAT software (Microsoft ® , version base 2020. 4. 1), and a two-sided p-value < 0. 05 was considered statistically significant.
, 2010; Di Pierro et al. , 2021). The natural senolytic agent, quercetin, an effective antiviral and anti-neurodegeneration compound, is currently in clinical trials for COVID-19  (Khan et al. , 2019; Islam et al. , 2021 ) (NCT05037240). Quercetin was found to preempt the development neuronal damage as well as to possess anticancer and anti-inflammatory properties  (Vafadar et al. , 2020). A novel senolytic vaccine, recently tested in progeroid mice, may usher a new era in senolytic interventions as it opens the possibility of preventing the development of neurodegenerative disorders, viral infections and possibly cancer  (Yoshida et al. , 2020; Suda et al. , 2021). Furthermore, an antibody-drug conjugate against a membrane senescence marker was demonstrated to clear senescent and virus-infected cells, emphasizing a new senolytic strategy  (Poblocka et al.
Microtubule Stabilizing Agents MSAs are compounds that attach to the microtubules, preventing their disassembly. Most drugs targeting microtubules are anticancer agents that may also possess anti-neurodegenerative and antiviral effects  (Hung and Fu, 2017; Fernandez-Valenzuela et al. , 2020; Tsai et al. , 2020; Sirakanyan et al. , 2021). Many of these drugs demonstrated beneficial effects in animal models, however those tested in humans are few and include TPI-287 and NAP (Davunetide CP201), an intranasal neuropeptide (NAPVSIPQ) (NAP) (NCT01966666)  (Gozes, 2020). These MSAs have not reached the clinic, however, activity-dependent neuroprotective protein (ADNP), derived from NAP, remains a potential hope and is scheduled for future clinical trials  (Varidaki et al. , 2018; Al-Horani and Kar, 2020; Santiago-Mujika et al.
, 2021). A recent addition to MSAs, sabizabulin, is currently in clinical trials as an   (Sargiacomo et al. , 2020)  antiviral drug, suggesting possible benefit in tauopathies (NCT04388826)  (Malebari et al. , 2021). Natural MSA compounds, CNDR-51549 and CNDR51555 (US patent: US20170173016 A1) were found to cross the blood brain barrier, indicating potential benefit in tauopathies  (Kovalevich et al. , 2016). Another compound, CNDR-51657, was demonstrated to downregulate the hyperphosphorylated Tau, suggesting a preventive potential  (Zhang et al. , 2018). Another natural MSA compound and Tacca extract, taccalonolides, may benefit AD patients by augmenting tubulin polymerization, reversing the effect of pTau  (Murru et al. , 2020; Chen et al. , 2021). Interestingly, studies from the 1970s observed that lithium was a MSA, raising interesting questions about its established antiviral and neuroprotective properties  (Matsunaga et al.
This is illustrated by the novel recombinant anti-HERV-W ENV antibody (GNbAC1), currently in clinical trials for MS, and a promising antiviral agent (NCT01639300)  (Diebold and Derfuss, 2019; Singh and Bharara Singh, 2020). The same may be true of arginase inhibitors, MSA, and TMEM16F inhibitors, drugs with multiple therapeutic targets. At present, the study of cell-cell fusion is in its infancy therefore, a better understanding of the molecular underpinnings of syncytia formation would shed light on cellular uptake of pathogens and oncogenes, opening novel avenues for preventive care.
More neuron functional clusters occurred in Tat 3 Coc, including neuron projection development, neuron development, neurogenesis, and dendrite development (right panel, Figure  3B ), which provides molecular evidence to support our behavioral findings that chronic cocaine use led to more severe cognition decline in iTat mice. Different to GO functional clusters, KEGG pathway analysis revealed more convergent effects among these three factors, as the extracellular matrix (ECM) receptor-ligand interaction pathway was found in all of them (Figures  3C  and S5D ). ECM is deeply involved in synapse formation and plasticity  (Ferrer-Ferrer and Dityatev, 2018). These findings suggest that Tat, Coc, and Tat 3 Coc affect neuronal function commonly through the ECM receptor interaction pathway. To determine the roles of different cell types in response to Tat and cocaine, we employed machine learning to build up the database of different brain cell types and then used this database to tag the above overlapped genes into neuron, microglia, astrocyte, oligodendrocyte, oligodendrocyte progenitor cell (OPC), and endothelial cell.
Specifically, we took the top 6,000 ranked cell-type-enriched mouse genes  (McKenzie et al. , 2018) , mapped them with the RNA-seq transcriptome database from purified brain cells  (Zhang et al. , 2014) , employed a support vector machine  (Zhang et al. , 2021)  to build a learning model with these mapped genes, and applied the trained model to predict the cell type for the unmapped genes. The largest numbers of genes were in microglia for all three factors (Figure  3D ), suggesting that microglia was the most affected cell type by Tat, Coc, and Tat 3 Coc. All the other five cell types appeared to be somewhat equally affected among these three factors. To identify the most promising gene targets for the morphological changes of microglia, astrocyte, and neuron and mouse behavior, we used a volcano map to sort out genes with q < 1e-5 DMRs and q < 0.
Sequencing data processing and analysis A bioinformatics analysis pipeline nf-core/methylseq  (Ewels et al. , 2020)  was used to process the methylation (Bisulfite) sequencing data. Briefly, we input the FASTQ raw data, aligned the reads, and performed extensive quality-control on the results. In the pipeline, FASTQC (v 0. 11. 9) was chosen for quality control and Trim Galore! (v0. 6. 5) for adapter sequence trimming with default parameters. Trimmed sequences were then mapped to the Mus musculus assembly (GRCm38/mm10) from Genome Reference Consortium, using Bismark 0. 22. 3 (Krueger and Andrews, 2011)  with the alignment tool Bowtie2 (v2. 4. 2)  (Langmead and Salzberg, 2012). Sequence duplicates were further removed by command ''deduplicate_bismark'' and context-dependent methylation (CpG and CpH) were extracted by command ''bismark_methylation_extractor''.
The fixed effects in equation are m (mean), S i (sex, male or female), P j (genotype, iTat or Wt), T k (treatment, Coc or Sal), PT jk (genotype-treatment interaction),SP ij (sex-genotype interaction), and ST ik (sex-treatment interaction); i = 1,2 stands for male and female, respectively; j = 1,2 stands for iTat and Wt mice, respectively; and k = 1,2 stands for Coc and Sal treatment, respectively. The variance components in the linear mixed model were estimated using the residual maximum likelihood (REML) approach  (Pinheiro and Bates, 2002). We used R program to fit linear mixed models to each site and preset region. The lsmeans package was used for testing the differences in combinations of levels among genotype and treatment, and for tests of Difference-in-Differences in the interaction between genotype and treatment.
Annotation with custom regions Our preset regions were downloaded from annotatr package  (Cavalcante and Sartor, 2017) , which provides genomic annotations including 1 to 5 kb, promoters, 3UTRs, 5UTRs, exons, exon boundaries, introns, and intergenic. Then, three critical regions, including promotors, exons, and introns were parsed and merged with GRCm38 gene annotation file for region analysis. RNA sequencing (RNA-Seq) data processing Reads were mapped to GRCm38 reference genomes with STAR (v2. 7. 9)  (Dobin et al. , 2013). The maximum number of multiple alignments allowed for a read is set to 10. And, featureCounts (v 2. 0. 1. 13) was used to quantify reads with the following parameters (-T 10 -t exon -s 1 -g exon_id). The read count matrix was then normalized by countToFPKM package (v 1.
Gene-linked DMR To determine the potential genes which were possibly regulated by differentially methylated regions (DMR). We defined Genelinked DMR as the differentially methylated regions mapped to or close to genes that are differentially expressed (DEG) in RNA-Seq. Gene expression values were measured at the exon level from RNA-Seq by feature Counts, in order to increase mapping with DMR. We merged the DMR annotated from annotatr package  (Cavalcante and Sartor, 2017)  and differentially expressed exons from RNA-Seq by transcript ID. There were totally four types of gene-linked DMR: Hyper-methylated DMR (Hyper-DMR) linked to up-regulated genes (Up-DEG), Hyper-DMR linked to down-regulated genes (Down-DEG), Hypo-methylated DMR (Hypo-DMR) linked to Up-DEG, and Hypo-DMR linked to Down-DEG.
Machine learning for cell type prediction In order to predict the cell types for gene-linked DMR, we first downloaded the top 6000 ranked cell type-enriched mouse genes from the reference  (McKenzie et al. , 2018)  and mapped them with the RNA-Seq transcriptome database of purified cell classes of the brain  (Zhang et al. , 2014). The Support Vector Machine  (Zhang et al. , 2021)  was used to build a learning model from the mapped genes, and then the trained model was applied to predict the cell type for the unmapped genes and generated a database with cell type information. The following parameters were used for training: method = ''C-classification'', kernel = ''radial'', scale = TRUE, gamma = 1, and cost = 1000. ''Astro. 1'', ''Astro. 2'', ''Neuron. 1'', ''Neuron. 2'', ''OPC. 1'', ''OPC.
2'', ''NFO. 1'', ''NFO. 2'', ''Myelinating. 1'', ''Myelinating. 2'', ''MGL1'', ''MGL2'', ''Endo. 1'', ''Endo. 2'' were used for features. An internal five-fold cross-validation method evaluated that its prediction accuracy could reach 80%. Finally, we mapped the gene-linked DMR to this database. Finally, we mapped the genelinked DMR to this database.
Concerning ICIs resistance in solid tumors, the review by Roberto et al. analyzes how microbiota is affected by intestinal microenvironment and how microenvironment alterations may influence the response to ICIs. They showed how diet is emerging as a fundamental determinant of microbiota's community structure and function and describe the role of certain dietary factors, as well as the use of probiotics, prebiotics, postbiotics, and antibiotics in modifying the human microbiota. Finally, they shed new light on the possibility of administering fecal microbiota transplantation to modulate the gut microbiota in cancer treatment. Within the frame of this Research Topic, the article of Qing et al. probed the Cancer Genome Atlas (TCGA) and the GEO repository for gene signatures relating to angiogenesis and immune cells infiltration and combined the transcriptomic data with prognostic data to predict therapeutic responses.
Glycolipid-specific T cells express a diverse TCR repertoire To profile CD4 and CD8 glycolipid-specific T cells directly ex vivo, we sorted single cells from four South African adults with newly diagnosed active TB disease using SGL-CD1b and GMM-CD1b tetramers  (Day et al. , 2011). In this experiment, we included GMM-specific T cells and T cell lines with known TCRs as controls to verify our tetramer-sorting strategy. Gates defining CD1b tetramer-positive cells were set based on control samples not stained with the tetramers as well as SGL-and GMM-specific T cell lines (Supplemental Figure  1 ). To validate our sorting strategy, we first assessed the TCRs recovered from the T cell lines with a known TCR. We note that the V genes, J genes, and CDR3s recovered from these wells match the known TCRs (Supplemental Data 1).
0001, n = 29,195). A no tetramer control is also represented (light grey) (right). The distributions of mTCRBC MFI of the DN (dark grey) and CD8 (white) Jurkat cells were also compared (right) (Student's t-test, p < 0. 0001, n = 29,195). (C) The distributions of SGL-CD1b MFI of CD8 hi (dark grey, CD8 MFI of 10 4 to 10 5 ), CD8 med (grey, MFI of 10 3 to 10 4 ) and CD8 lo (light grey, MFI of 0 to 10 3 ) Jurkat cells were then compared (right) (Student's t-test, p < 0. 0001, n = 20,260). A no tetramer control is also represented (grey) (right). The distributions of mTCRBC MFI of CD8 hi , CD8 med , and CD8 lo Jurkat cells were then compared (right) (Student's t-test, p < 0. 0001, n = 20,260). Data are representative of three independent experiments. (white) and A05 (black) T cell lines was measured using intracellular cytokine staining after co-incubation with K562-CD1b or K562-EV antigen presenting cells and SGL antigen for 6 hours.
Data are expressed as a percent of T cells that express the cytokine as a percentage of total CD3+ T cells. Percentages are background corrected by subtracting the percentage of T cells that express cytokine after co-incubation with K562-EV antigen presenting cells and SGL for 6 hours. Error bars represent the mean and standard deviation of four technical replicates. (* = p = 0. 03, Mann Whitney, n = 8). Data are representative of at least two independent experiments. (B) Granzyme B (GzmB) production from 1,000 T cells from the A01 (white) and A05 (black) T cell lines after co-incubation with K562-CD1b antigen presenting cells and SGL antigen. Cells were cultured overnight and GzmB production was assessed using GzmB ELISPOT. Antigen concentration ranged from 1 µg/ml to 10 -5 µg/ml in log-fold dilutions.
TABLES Table 1. TCRs expressed by SGL-specific T Cell Lines Clonality was determined by via staining with antibodies targeting human TCR-β variable genes (IOTest BetaMark Kit, Beckman Coulter) or high-throughput sequencing (ImmunoSEQ, Adaptive Biotechnologies). The full-length TCR sequence was determined by template-switched PCR after sorting tetramer-positive T cells. The T cell receptor gene usage and CDR3 region amino acid sequence is summarized here. Variable and joining gene segment names were assigned using the International ImMunoGeneTics (IMGT) Database. Lines TCR Variable CDR3 Joining A01 ɑ TRAV8-6 CAVKAGYSSASKIIF TRAJ3 β TRBV12-4 CASKGKQGPEQFF TRBJ2-1 A05 ɑ TRAV13-2 CAEVLGGTSYGKLTF TRAJ52 β TRBV20-1 CSASGRRGYGYTF TRBJ1-2.
2% BSA (Sigma, St. Louis, MO)) mixed 1:1 for 10 minutes at 4°C. Cells were then resuspended in 50 µl of FACS buffer containing 1 µl of SGL-loaded CD1b and 1 µl of mock-loaded control CD1b tetramers, then incubated at room temperature for 60 minutes. The tetramer titers were determined prior to use in the present study (data not shown). Following a 15 minute incubation at room temperature, the cells were washed twice in PBS and then stained with anti-CD3 ECD (Beckman Coulter, Brea, CA), CD4 APC Ax750 (Beckman Coulter, Brea, CA) and anti-CD8a PerCP Cy5. 5 (BD Biosciences, San Jose, CA). When T cells transduced with exogenous TCR were used, anti-mouse TCR β chain APC (BD Biosciences, San Jose, CA) was also included in the antibody cocktail. The optimal titers of all antibodies were determined prior to use (data not shown).
The variable genes, joining genes, and CDR3 used by the sequences were analyzed using the IMGT Database (Montpellier, France). T cell Receptor Cassette Construction. Codon-optimized A01 and A05 T cell TCR sequences were assembled into a TCR cassette  (Linnemann et al. 2013). In this construct, the TCR α and TCR β chain constant regions are replaced with modified murine TCR ɑ chain and TCR β chain constant regions (mTCRBC) to facilitate measurement of TCR expression by flow cytometry. It also contains an extra cysteine residue to encourage pairing between exogenous TCR chains. The TCR cassettes were synthesized through Thermo Fisher GeneArt Synthesis service and were then cloned into pRRL. PPT. MP. GFPpre  (Jing et al. , 2016; Zhou et al. , 2012)  using BamHI and SalI restriction enzymes (New England BioLabs, Ipswich, MA).
All plasmids were purified using Maxi Prep kits (Qiagen, Hilden, Germany). GenBank accession number for the A01 and A05 TCR constructs is pending. Generation of Lentivirus. Lenti-X HEK293T cells (Clontech, Mountain View, CA) were seeded at 2 million cells per 100 mm tissue culture dish and incubated for 48 hours at 37˚C/5% CO2 in DMEM (Gibco, Waltham, MA) or until cells reached 75% confluency. The medium was replaced 4 hours before transfection. Cells were transfected with 10 µg pRRL-TCR plasmid, 5 µg pCI-VSVG envelope plasmid, and 5 µg of a psPAX2 packaging vector (gifted from Dr. Stanley Riddell at Fred Hutchinson Cancer Research Center). Plasmids were mixed with Fugene 6 transfection reagent (Promega, Madison, WI) at a dilution of 1:12 in a total volume of 600 µl. Transfection mixture was added dropwise into the cell culture and incubated overnight in the conditions described above.
Transduction of Primary T cells. Cryopreserved PBMC from an anonymous blood bank donor (Bloodworks Inc, Seattle, WA) was thawed as described above and enumerated using trypan blue exclusion. T cells were isolated from PBMC using magnet activated cell sorting (MACS) using the Pan T cell Isolation Kit (negative selection) according to the manufacturer's instructions  (Miltenyi Biotec, Germany). Following separation, T cells are enumerated using Trypan Blue exclusion and co-incubated with CD3/CD28 Human T cell Activating Dynabeads (ThermoFisher) at a 3:1 bead:T cell ratio in a total volume of 1 ml TCM supplemented with 50 units/ml recombinant IL-2 in a 48-well plate. On day 2, lentiviral stock and 4 µg/ml polybrene was added to culture. Viral titer is determined prior to use (data not shown).
Single-cell Computational Methods Raw reads were trimmed and demultiplexed using VDJFasta software  (Glanville et al. , 2009). Paired end reads were assembled by finding a consensus of at least 100 bases. Amplicons smaller than 100 bases were removed. The resulting paired-end reads were then assigned to wells according to barcodes that designate the plate, row, and column. The average read count for this study was approximately 47,000 reads per well. To map the TCR reads, a cutoff of >95% sequence identity was established to collapse reads and establish a consensus sequence within each well. All sequences exceeding 95% sequence identity are assumed to derive from the same TCR sequence. The 95% cutoff conservatively ensures all sequences derived from the same transcript would be properly assigned.
In addition, TCR assignments that fail to pass a minimum frequency of 0. 1 for each well are removed from the dataset as they might be derived from sorted doublets  (Han et al. , 2014). For this dataset, the median frequency of the dominant TCR frequency in our dataset is 0. 79 with a range of 0. 313-0. 999. For phenotyping transcripts, the number of reads containing a 95% match to the customized database of transcription factor and cytokine genes are scored. Genes profiled in this study are summarized in Supplemental Tables  5  and 6. The resulting data is then compiled into a. csv file containing the TCR assignment and number of reads for each phenotype gene for each well. A minimum read count of five reads per gene was used to define expression of each gene as established in  Han et al.
, 2014. Data were then input into the R programming environment and further processed and formatted. The resulting matrix was then analyzed using Seurat to cluster the data and perform feature selection for two analyses  (Satija et al. , 2015). The first was unsupervised and included all cells that passed our QC criteria (n = 272). The second was a supervised analysis that only included cells where either CD4 or CD8 was detected (n = 105). The proportion of cells from the tetramer-positive and tetramer-negative groups that were included in the final analysis were similar (39% and 37%, respectively). Annotated data, as well as the code used to generate these data and figures are available at: https://github. com/chetanseshadri. Other data visualization and statistical tests were performed in Graph Pad Prism (v6) (San Diego, CA).
(H) SGL-CD1b tetramer positive T cells in the blood were single-cell sorted from cryopreserved PBMC donated by South African adults with new diagnosis of active TB disease (n = 2). This single cell sort was also indexed, meaning that the MFIs for each channel were saved for each single cell, and this information was combined with the TCR assignments to assign a co-receptor group to a cell. The negative population (black) is visualized here using a no tetramer control for each individual  (TB-1124, left; TB-1127, right). The sorted cells (red) are visualized within the sort gate (n = 184 per donor). (Gras et al. , 2016; Van (n = 149) , indicating that all functional clusters are represented by both patient samples and the samples are relatively evenly represented within the dataset. (C) All seven clusters are present among GMM-(n = 96) and SGL-specific T cells (n = 127), suggesting that there may be few functional differences between T cells that recognize both antigens, and that T cells specific for both antigens are evenly represented within the data set (middle).
Cells were cultured overnight and GzmB production was assessed using GzmB ELISPOT. Antigen concentration ranged from 1 µg/ml to 10 -5 µg/ml in log-fold dilutions. Error bars represent standard deviation of triplicate wells. Data are representative of two independent experiments. (C) Cytotoxicity was assayed by co-incubating A01 (left) and A05 (right) T cell lines with K562-EV and K562-CD1b antigen presenting cells labeled "low" and "high" with Cell Trace Violet, respectively. Co-cultures were incubated in the presence (red) or absence (blue) of SGL antigen. We defined specific lysis as the ability of a T cell line to specifically reduce the K562-CD1b cell population in the presence of lipid, while leaving the K562-EV population and the K562-CD1b without pulsed antigen intact after co-incubation.
ACKNOWLEDGEMENTS The authors thank  Krystle Yu ,  Paula Marsland ,  Natalie Erjavec ,  Sarah Roepke , and  Martin Prlic  for their technical assistance,  D. Branch Moody  for supplying purified GMM for these studies, and  Stephen De Rosa  for supplying the Seattle Assay Control samples. This work was supported by the  U. S. National Institutes of Health  ( R01AI12518904  to CS) and the  Doris Duke Charitable Foundation  (Grant No. 2016103  to CS). CAJ was supported by the  Institute of Translational Health Sciences  ( 5TL1TR00231803 ) and the  Molecular Medicine Training Program  ( T32GM095421 ).