ABSTRACT: The goal of this study was to determine changes in the expression of genes in monoctic myleoid derived suppressor cells (M-MDSC) as a result of SARS CoV2 infection. The study aimed to investigate if M-MDSC are functionally active and inhibit T cell function in response to SARS CoV2 antigens 5 months after first detection of the virus. Methods: Peripheral blood mononuclear cells (PBMC) were collected from CoV2 (-) and CoV2 (+) donors (N=5 each group). M-MDSC were isolated by flow cytometry, and RNA extracted for RNA-seq studies. Filtering low quality reads and removal of the 3’ adapter sequences were performed using the Trim Galore tool. Reads were mapped to the latest version of the human genome (build hg38) using HISAT2. Mapped reads were counted against the human GENCODE annotation (v37) using HT-Seq. The EdgeR library in the R computing environment was used for quality control of the RNA-Seq data, and ComBat-seq method for correction of batch effects. Differential gene expression analysis was conducted using EdgeR. Pathway enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8. Results: An average of 34 million reads per sample were acquired and mapped to the human genome (build hg38). After applying filtering criteria, 9,217 human genes were identified with the HISAT2 and HTSeq workflow. Differential expression analysis was performed between CoV2 (+) and CoV2 (-) samples using EdgeR. A total of 188 differentially expressed genes (DEGs) were identified with nominal p-value <0.05; of which 63 were up- and 125 downregulated in CoV2 (+) samples. A total of 12 DEGs were identified with false discovery rate corrected p-value <0.05, of which 2 were up- and 10 downregulated. Pathway enrichment analysis identified pathways involved in immune response and innate immune signaling. Conclusion: The study demonstrated that CoV2 infection modulated the expression of genes involved in immune response and innate immune signaling. Most of the genes remained downregulated even after 5 months of first detection of SARS CoV2.
Project description:The goal of this study was to investigate transcriptome remodeling induced by treatment with a camptothetin analog, Topotecan (TPT), of a primary T cell model of HIV latency. The study aimed to determine whether TPT has a global inhibitory effect on gene expression in primary CD4+ T cells, and identify mechanisms of action of this drug as an HIV “block and lock” agent. Methods: CD4+ T cells were isolated from blood of HIV seronegative study participants (N=3) and utilized to generate an in vitro model of latent HIV infection (model developed in the Spina laboratory and previously described by Spina et al., 2013 and Soto et al., 2022). Following generation of the model, cells were treated with 10 uM Topotecan or its solvent dimethyl sulfoxide (DMSO) for 24 hours. Cells were lyzed with RLT buffer containing beta-mercaptoethanol from a RNeasy micro kit (Qiagen, Inc. cat # 74004). ERCC spikes (Thermo Fisher Scientific, Inc.) were added to cell lysates based on cell number in each sample (10 ul of 1:800 dilution per million cells). RNA was extracted and subjected to deep sequencing at the Institute for Genomics Medicine (IGM) Genomics Center at the University of California San Diego. Filtering low quality reads and removal of the 3’ adapter sequences were performed using the Trim Galore tool. Reads were mapped to the human genome (build hg38) using HISAT2, and to HIV and ERCC references using bowtie. Mapped human reads were counted against the human GENCODE annotation (v37) using HT-Seq. ERCC counts were used to determine whether TPT had global inhibitory effect on transcription in primary CD4+ T cells. Because no such effect was observed, differential gene expression analysis was performed using library EdgeR in Bioconductor R without correction of expression based on ERCC spikes. Genes with false discovery rate (FDR)-corrected p-value less than 0.05 and an absolute fold change in TPT-treated samples compared to DMSO controls greater than 2, were considered significantly modulated by TPT. Pathway and gene ontology (GO) term enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v2022q3. Pathways and GO terms with FDR-corrected p-value less than 0.05 were considered significantly enriched for differentially expressed genes. Results: An average of 19.7 million reads per sample were acquired and mapped to the human genome (build hg38). After applying filtering criteria, 9,678 human genes were identified with the HISAT2 and HTSeq workflow. Differential expression analysis was performed between TPT- and DMSO-treated samples using EdgeR. A total of 1,749 differentially expressed genes (DEGs) were identified with FDR p-value <0.05 and an absolute fold change greater than 2; of which 522 were up- and 1227 downregulated by TPT. Pathway and GO term enrichment analysis revealed that TPT interferes with gene transcription and cell signaling pathways (e.g. T cell receptor signaling, positive regulation of JNK cascade, regulation of actin cytoskeleton and positive regulation of GTPase activity were affected by TPT treatment). Conclusion: The study demonstrated that TPT induces multiple effects on transcriptome in primary CD4+ T cells. Some of these changes may represent direct and indirect mechanisms of action of TPT as an HIV “block and lock” agent.
Project description:The aim of this study is to compare the NGS-derived profiling of human transcriptome (RNA-seq) in wild-type neuroblastoma cells with neuroblastoma transcript with a high level of expression of Oct-1 transcription factor and evaluate the effect of Oct-1 transcription factor on the regulation of nerve cell differentiation. Methods: Human mRNA profiles of 16-day differentiating wild-type neuroblastoma IMR32 and neuroblastoma IMR32 with overexpression primate-specific isoform of transcription factor Oct-1 were generated by deep sequencing, in triplicate, using Illumina NovaSeq. Mapping reads to the human genome (hg38) using hisat program. On average, about 89-90% of all received data was uniquely aligned in each library. The htseq-count utility calculated the number of reads that were mapped to known genes (ncbi - entrezID). The obtained values (cpm - countpermillion) for each gene for each library were combined into one matrix for further analysis. Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the human genome (hg38). Filtration, normalization by the method (TMM), variance estimation and evaluation of differentially expressed genes were performed in the edgeR module. Genes in which cpm did not exceed 1 in any three libraries were considered low-expressing. After filtering the low-expressing genes, 15,108 entries remained. RNA-Seq data confirmed that approximately 8% of the transcripts showed differential expression between the WT and Oct-1 overexpressed differentiating neuroblastoma cells, with FDR<0.01 (p-value adjusted for multiple testing).
Project description:Purpose: The goals of this study are to obtain the NGS-derived transcriptome profiling (RNA-seq) for THP-1 macrophages response to Mycobacterium tuberculosis (H37Rv and H37Ra) Methods: mRNA and long noncoding RNA profiles of THP-1 macrophages infected with H37Rv and H37Ra for 1, 4, 12, 24, 48 hours were generated by deep sequencing, using Illumina Hiseq3000. The sequence reads that passed quality filters were first mapped to the latest UCSC transcript set using Bowtie2 (version 2.1.0). Then the gene expression level was estimated using RSEM (RNA-Seq by Expectation Maximization, v1.2.15), for lncRNA analysis, reads were mapped to lncRNA transcript set from LNCipedia.org. The sequence reads were normalized with TMM (trimmed mean of M-values) to identify differentially expressed genes (DEGs) using the edgeR package edgeR. qRT–PCR validation was performed using SYBR Green assays. Results: Using an optimized data analysis workflow, we mapped about 20 million sequence reads per sample to the human genome (GRCh38/hg38) and identified 25,343 mRNA and 47877 long non-coding RNA transcripts. Conclusions: Our study represents the detailed analysis of transcriptomes for THP-1 macrophages response to H37Rv and H37Ra, generated by RNA-seq technology.
Project description:Purpose: The goals of this study are to obtain the NGS-derived transcriptome profiling (RNA-seq) for THP-1 macrophages response to early secreted antigenic target 6-KDa (ESAT6) from Mycobacterium tuberculosis Methods: mRNA profiles of THP-1 macrophages treated with ESAT6 were generated by deep sequencing, using Illumina Hiseq3000. The sequence reads that passed quality filters were first mapped to the latest UCSC transcript set using Bowtie2 (version 2.1.0). Then the gene expression level was estimated using RSEM (RNA-Seq by Expectation Maximization, v1.2.15), and normalized with TMM (trimmed mean of M-values) to identify differentially expressed genes (DEGs) using the edgeR package edgeR. qRT–PCR validation was performed using SYBR Green assays. Results: Using an optimized data analysis workflow, we mapped about 23 million sequence reads per sample to the human genome (GRCh38/hg38) and identified 25,343 transcripts. Conclusions: Our study represents the first detailed analysis of transcriptomes for macrophages response to ESAT6, generated by RNA-seq technology.
Project description:Purpose: The goals of this study are to obtain the NGS-derived transcriptome profiling (RNA-seq) for THP-1 macrophages, which were stimulated from phagosome with early secreted antigenic target 6-KDa (ESAT6) Methods: First we fabricated a nanocapsule enclosing ESAT6 (nESAT6) and a nanocapsule enclosing BSA (nBSA) as control. mRNA profiles of THP-1 macrophages treated with nESAT6 were generated by deep sequencing, using Illumina Hiseq3000. The sequence reads that passed quality filters were first mapped to the latest UCSC transcript set using Bowtie2 (version 2.1.0). Then the gene expression level was estimated using RSEM (RNA-Seq by Expectation Maximization, v1.2.15), and normalized with TMM (trimmed mean of M-values) to identify differentially expressed genes (DEGs) using the edgeR package edgeR. qRT–PCR validation was performed using SYBR Green assays. Results: Using an optimized data analysis workflow, we mapped about 23 million sequence reads per sample to the human genome (GRCh38/hg38) and identified 25,343 transcripts. Conclusions: Our study represents the first detailed analysis of transcriptomes for macrophages response to ESAT6 stimulation in phagosome, generated by RNA-seq technology.
Project description:mRNA-seq were conducted for iPS cells of human-1 (409-B2/HPS0076), human-2 (Nips-B2/HPS0223), chimpanzee-1 (kiku/0138F-1), and chimpanzee-2 (mari/0274F-2). To compare gene expression levels, the reads were first mapped to the chimpanzee genome (panTro5), and mapped reads were then mapped to the human genome (hg38). Gene expression was anlyzed based on the hg38 annotation.
Project description:Library preparation for ATAC-Seq was performed on 1000-5000 cells with Nextera DNA Sample Preparation kit (Illumina), according to previously reported protocol55. 4 ATAC-seq libraries were sequenced per lane in HiSeq 2500 System (Illumina) to generate paired-end 50-bp reads. Reads were mapped to hg38 using BWA (0.7.15) using default parameters. Duplicate reads, reads mapped to mitochondria, an ENCODE blacklisted region or an unspecified contig were removed (Encode Project Consortium, 2012). MACS (2.2.5) was used to call peaks in mapped reads.
Project description:Purpose: Transcriptomic analysis of clinical tissue samples is important in the precision medicine research. To explore the relationship between transcriptome and full proteome, 51 LUAD tumor tissues and 49 paired non-cancerous adjacent tissues were systematically analyzed by RNA sequencing. Methods: RNASeq reads were adapter trimmed and data quality was assessed with the FastQC (version 0.11.7) software before any data filtering criteria may apply. Reads were mapped onto the human reference genome (GRCh38.p12 assembly) by using hisat2 software (v2.0.4). The mapped reads were assembled into transcripts or genes by using StringTie software (v1.3.4d) and the genome annotation file (hg38_ucsc.annotated.gtf). For quantification purpose, the relative abundance of transcript/gene was measured by a normalized metrics, FPKM (Fragments Per Kilobase of transcript per Million mapped reads). Results: In our study, RNA-seq analysis identified 16,188 genes with FPKM > 1, providing an opportunity to explore the relationship between the transcriptome and full proteome. Conclusions: Our study provided an opportunity to explore the relationship between the transcriptome and full proteome of clinical LUAD tumors and their paired non-cancerous adjacent tissues.
Project description:Purpose: The goals of this study are to monitor the evolution pattern of SARS-CoV2 in depending host cells by viral transcriptome sequencing analysis of Vero, A549, Caco2, and HRT18 cells infected with SARS-CoV2. Methods: SARS-CoV-2 isolate was passaged 4 time on Vero cells and used to extract RNA for the high-throughput sequencing. The 8×104 PFU of SARS-CoV2 stocks passaged on vero cells were inoculated to the monolayer of A549, CaCO2, and HRT-18 cell lines in 75T flask for 1hour at 37℃ in a 5% CO2 incubator with gentle shaking of 15 minutes interval. After that, the infected cells were washed two times with DPBS and incubated with the fresh maintenance medium for 3 days. The virus inoculation was performed in triplicate for each cell lines. In case of the first passage, the infected cell pellets were resuspended to 250µl with fresh medium, to extract RNA for the high-throughput sequencing. The cultured cell supernatant of the virus-infected A549, CaCO2, and HRT18 cells was centrifuged at 3,000g for 10min to use for the next passage, and stored at -80℃. The serial passage of SARS-CoV-2 on A549, CaCO2, and HRT18 cell lines were continued to passage 12 and the cultured cell supernatant of the infected cells in passage 12 was centrifuged at 3,000g for 10 min, and used to extract RNA for the high-throughput sequencing. The RNA samples were sequenced with illumine TruSeq Strand Total RNA LT kit and illumine NovaSeq6000 plaform form Macrogen, Inc (Seoul, Korea) for high throughput sequencing. The raw reads were trimmed with BBDuk and mapped the isolate SARS-CoV-2/human/KOR/KCDC03-NCCP43326/2020 (Genebank accession number. MW466791) with Bowtie 2 using Geneious program 2021.2.2 Result: Using SNP analysis workflow, our result showed the sequence variations pattern of SARS-CoV2 depending on host cell (A549, CaCO2, and HRT18 cell lines) and it was confirmed that a relatively large number of SNPs were commonly observed in spike protein. Some SNPs affect amino acid changes, and a common pattern of amino acid changes was observed the genomic sequence of SARS-CoV2 passaged in A549, CaCO2 and HRT18 cells. Conclusion: In this study, we tried to monitor the SARS-CoV-2 (GenBank accession No. MW466791 in 2020, Korea) evolution pattern in different host cells using high throughput sequencing analysis, and compare the selected mutations by each host cells with natural mutations found in currently circulating SARS-CoV-2 variants.
Project description:The goals of this study are to compare transcriptome profiling (RNA-seq) of seletive BET inhibitior, combiantion of BET and p300 inhibitor, and dual BET/p300 inhibitor in pancreatic cancer model (PANC1 cells) and study the anticancer mechanism. Using an optimized data analysis workflow, we mapped about 20 million sequence reads per sample to the human genome (build hg38) using Burrows Wheeler Aligner- Maximum Exact Match (BWA MEM) algorithm. The raw count for each gene was quantified using the general purpose read summarization function, featureCounts gainst ENSEMBL genes. Differential analysis was performed using edgeR, including a batch effect correction for batch effects. Geneset enrichment analysis based on the differential analysis idenfied few essential pathways that was the mechansim of action, including KRAS, TGFb, etc.