NcRNA-Seq of human blood with different disease state of COVID-19
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ABSTRACT: To analyse gene expression pattern in different disease state of COVID-19 patients. Experimental workflow: 1) rRNA was removed by using RNase H method, 2) QAIseq FastSelect RNA Removal Kit was used to remove the Globin RNA, 3) The purified fragmented cDNA was combined with End Repair Mix, then add A-Tailing Mix, mix well by pipetting, incubation, 4) PCR amplification, 5) Library quality control and pooling cyclization, 6) The RNA library was sequenced by MGI2000 PE100 platform with 100bp paired-end reads. Analysis steps: 1) RNA-seq raw sequencing reads were filtered by SOAPnuke (Li et al., 2008) to remove reads with sequencing adapter, with low-quality base ratio (base quality < 5) > 20%, and with unknown base (’N’ base) ratio > 5%. 2) Reads aligned to rRNA by Bowtie2 (v2.2.5) (Langmead and Salzberg, 2012) were removed. 3) The clean reads were mapped to the reference genome using HISAT2 (Kim et al., 2015). Bowtie2 (v2.2.5) was applied to align the clean reads to the transcriptome. 4)Then the gene expression level (FPKM) was determined by RSEM (Li and Dewey, 2011). Genes with FPKM > 0.1 in at least one sample were retained.
Project description:To analyse gene expression pattern in different disease state of COVID-19 patients. Experimental workflow: 1) Small RNA enrichment and purification, 2) Adaptor ligation and Unique molecular identifiers (UMI) labeled Primer addition, 3) RT-PCR, Library quantitation and pooling cyclization, 4) Library quality control, 5) Small RNAs were sequenced by BGI500 platform with 50bp single-end reads resulting in at least 20M reads for each sample. Analysis steps: 1) Small RNA raw sequencing reads with low quality tags (which have more than four bases whose quality is less than ten, or have more than six bases with a quality less than thirteen.), the reads with poly A tags, and the tags without 3’ primer or tags shorter than 18nt were removed. 2) After data filtering, the clean reads were mapped to the reference genome and other sRNA database including miRbase, siRNA, piRNA and snoRNA using Bowtie2 (Langmead and Salzberg, 2012). Particularly, cmsearch (Nawrocki and Eddy, 2013) was performed for Rfam mapping. 3) The small RNA expression level was calculated by counting absolute numbers of molecules using unique molecular identifiers (UMI, 8-10nt). MiRNA with UMI count lager than 1 in at least one sample were considered as expressed.
Project description:Purpose: To explore myokines and signaling pathways in skeletal muscle dysfunction in a Cigarette Smoke-induced Model of Chronic Obstructive Pulmonary Disease Methods: Total RNA was extracted from the muscle tissues using Trizol (Invitrogen, Carlsbad, California, USA) according to manual instruction. About 60 mg of tissues were ground into powder by liquid nitrogen in a 2 mL tube, followed by being homogenized for 2 minutes and rested horizontally for 5 minutes. The mix was centrifuged for 5 minutes at 12,000×g at 4°C, and then the supernatant was transferred into a new EP tube with 0.3 mL chloroform/isoamyl alcohol (24:1). The mix was shacked vigorously for 15s, and then centrifuged at 12,000×g for 10 minutes at 4°C. After centrifugation, the upper aqueous phase where RNA remained was transferred into a new tube with equal volume of supernatant of isopropyl alcohol, then centrifuged at 13,600 rpm for 20 minutes at 4°C. After deserting the supernatant, the RNA pellet was washed twice with 1 mL 75% ethanol, and then the mix was centrifuged at 13,600 rpm for 3 minutes at 4°C to collect residual ethanol, followed by the pellet air dry for 5-10 minutes in the biosafety cabinet. Finally, 25μL~100μL of DEPC-treated water was added to dissolve the RNA. Subsequently, total RNA was qualified and quantified using a Nano Drop and Agilent 2100 bioanalyzer (Thermo Fisher Scientific, MA, USA). Results: The sequencing data were filtered with SOAPnuke (v1.5.2) (Li et al., 2008)by (1) Removing reads containing sequencing adapter; (2) Removing reads whose low-quality base ratio (base quality less than or equal to 5) was more than 20%; (3) Removing reads whose unknown base ('N' base) ratio was more than 5%, and afterwards clean reads were obtained and stored in FASTQ format. The clean reads were mapped to the reference genome using HISAT2 (v2.0.4)(Kim et al., 2015). Bowtie2 (v2.2.5)(Langmead and Salzberg, 2012) was applied to align the clean reads to the reference coding gene set,and then expression level of gene was calculated by RSEM (v1.2.12)(Li and Dewey, 2011). The heatmap was drawn by pheatmap (v1.0.8) according to the gene expression in different samples. Essentially, differential expression analysis was performed using the DESeq2(v1.4.5) with Q value ≤ 0.05. To take insight to the change of phenotype, GO (http://www.geneontology.org/) and KEGG (https://www.kegg.jp/) enrichment analysis of annotated different expressed gene was performed by Phyper (https://en.wikipedia.org/wiki/Hypergeometric_distribution) based on Hypergeometric test. The significant levels of terms and pathways were corrected by Q value with a rigorous threshold (Q value ≤ 0.05) by Bonferroni. Conclusions: Our study represents the first detailed analysis of retinal transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.
Project description:In order to identify differentially abundant proteins, human plasma samples from COVID-19 patients with either a mild or moderate (MM) or a critical or severe (CS) disease course from acute phase of infection were analyzed on antibody microarrays 998 different proteins by 1,425 antibodies.
Project description:To evaluate the gene expression profiling of peripheral leukocytes in different outcomes of SARS-CoV-2 infections, whole blood samples were collected from individuals with positive SARS-CoV-2 nasopharyngeal swab by RT-PCR (54 patients) and healthy uninfected individuals (12 volunteers). Infected patients were classified into mild, moderate, severe and critical groups according to a modified statement in the Novel Coronavirus Pneumonia Diagnosis and Treatment Guideline. Blood were collected into EDTA tubes and the buffy coat samples were stored in TRIzol reagent at -80 °C until use for RNA extraction. Affymetrix Clariom S array was used to perform the high-throughput gene expression profiling. Microarray analyses were performed using APT Affymetrix software, R packages and Bioconductor libraries. This systemic view of SARS-CoV-2 infections through blood transcriptomics will foster the understanding about molecular mechanisms and immunopathological processes involved in COVID-19 disease and its different outcomes.
Project description:Purpose: RNase Y is a major enzyme responsible for mRNA degradation in Streptococcus pyogenes. The goals of this study are to understand whether RNase Y plays a role in operon transcription of S. pyogenes NZ131 by using RNA-seq analysis. Methods: S. pyogenes mRNA profiles of wild type (WT) and RNase Y mutant (∆rny) were generated by deep sequencing, in duplicate, using Illumina Hiseq 2000. The sequence reads were aligned to the S. pyogenes genome using Bowtie2. The aligned files were sorted to BAM format and indexed using Samtools. The read depth of each base was derived from BAM files using BEDtools. Operon organization of S. pyogenes WT and ∆rny strains were predicted based on base reads. Results: A total of 11 to 12 billion reads were obtained from each sample. More than 99% of these reads were mapped to the S. pyogenes genome. Predictions of operon organization using WT and ∆rny samples showed little difference between the two strains. Conclusions: Our result shows that the mutation of RNase Y does not affect the operon organization of S. pyogenes NZ131.
Project description:We developed a software package STITCH (https://github.com/snijderlab/stitch) to perform template-based assembly of de novo peptide reads from antibody samples. As a test case we generated de novo peptide reads from protein G purified whole IgG from COVID-19 patients.
Project description:Staphylococcus aureus USA300 wild type strain was cultivated in RPMI medium in 3 biological replicates and harvested at an OD500 of 0.5 before (as control) and at 30 min after exposure to 1.5 mM in RPMI HOCl stress. Cells were disrupted in 3 mM EDTA/ 200 mM NaCl lysis buffer with a Precellys24 ribolyzer followed by RNA isolation using the acid phenol extraction protocol as described. The RNA quality was approved by Trinean Xpose (Gentbrugge, Belgium) and the Agilent RNA Nano 6000 kit using an Agilent 2100 Bioanalyzer (Agilent Technologies, Böblingen, Germany). Ribo-Zero rRNA Removal Kit (Bacteria) from Illumina (San Diego, CA, USA) was used to remove the rRNA. TruSeq Stranded mRNA Library Prep Kit from Illumina (San Diego, CA, USA) was applied to prepare the cDNA libraries. The cDNAs were sequenced paired end on an Illumina HiSeq 1500 (San Diego, CA, USA) using 70 and 75 bp read length and a minimum sequencing depth of 10 million reads per library. The paired end cDNA reads were mapped to the Staphylococcus aureus USA300 genome sequence (accession number FPR3757_CP255) using bowtie2 v2.2.7 (Langmead and Salzberg, 2012) with default settings for paired-end read mapping. All mapped sequence data were converted from SAM to BAM format with SAMtools v1.3 (Li et al., 2009) and imported to the software ReadXplorer v.2.2 (Hilker et al., 2016).
Project description:Staphylococcus aureus COL wild type strain was cultivated in LB medium in 3 biological replicates and harvested at an OD500 of 0.5 before (as control) and at 30 min after exposure to 1176 μM neutrophil-derived oxidant hypothiocyanous acid (HOSCN) stress. Cells were disrupted in 3 mM EDTA/ 200 mM NaCl lysis buffer with a Precellys24 ribolyzer followed by RNA isolation using the acid phenol extraction protocol as described. The RNA quality was approved by Trinean Xpose (Gentbrugge, Belgium) and the Agilent RNA Nano 6000 kit using an Agilent 2100 Bioanalyzer (Agilent Technologies, Böblingen, Germany). Ribo-Zero rRNA Removal Kit (Bacteria) from Illumina (San Diego, CA, USA) was used to remove the rRNA. TruSeq Stranded mRNA Library Prep Kit from Illumina (San Diego, CA, USA) was applied to prepare the cDNA libraries. The cDNAs were sequenced paired end on an NextSeq 500 (San Diego, CA, USA) using 75 bp read length and a minimum sequencing depth of 8 million reads per library. The paired end cDNA reads were mapped to the Staphylococcus aureus COL genome sequence (accession number CP000046) using bowtie2 v2.2.7 (Langmead and Salzberg, 2012) with default settings for paired-end read mapping. All mapped sequence data were converted from SAM to BAM format with SAMtools v1.3 (Li et al., 2009) and imported to the software ReadXplorer v.2.2 (Hilker et al., 2016).
Project description:The goals of this study are to comprehensively identify genes controlled by Jmjd6 in the thymic stroma, and to identify a novel alternative splicing mechanism. Methods: Samples were WT and Jmjd6-/- fetal thymus organ culture with (2 samples for each category) or without (1 sample for each category) RANKL stimulation for 4 days under 2-DG. One µg of total RNA was used for library construction with TruSeq RNA Sample Prep Kit v2. THe ligated products were amplified using 8 cycles of PCR to generate RNA-seq library. Library integrity was verified by Bioanalyzer DNA1000 assay. Sequencing was performed in 101-bp paired-end mode using an Illumina HiSeq.Technical duplicate has done. Results: A total of 177,060,020 reads were obtained for 6 samples. Filtered reads were mapped to the UCSC mm10 using the TopHat program(v2.0.10) with the default parameters. The Cufflinks program (v2.1.1) was then used to assemble 22,448 transcripts and to calculate the fragments per kilobase of exon per million mapped fragments(FPKM) values, which are normalized measurement of gene expression levels(= genes-FPKM file).To identify differentially expressed genes, the ratio of the maximum FPKM to the minimum FPKM was compared among 6 samples. When the ratio was more than 3, the gene was regarded as being significantly altered in expression level. We added 0.1 to the FPKM value to avoid division by zero. This led us to identify 3212 genes with differential expression. Among these, the expression levels of 2536 genes were significantly associated with the RANKL treatment or Jmjd6 expression ( P value <0.05). Analysis for intron retention was performed as follows. According to the current gene annotation ("known genes" in UCSC mm10), there are 188,208 introns in total. As intron retention events should be observed in the genes with relatively high expression, we only focused on the genes with the maximum FPKM value more than 10 at least in one of the six samples. As a result, we obtained 84,708 introns. The reads mapped to these intronic regions were counted by the intersectBed program in the BEDTools utilities with -c option, and the counts are converted into the FPKM values for each intron (intronic FPKM). There are 1051 introns with intronic FPKM more than 10 in at last on of the six samples, and the degree of intron retention was calculated by dividing intronic FPKM value by conventional FPKM value for each gene (intron-FPKM file). Samples were WT and Jmjd6-/- fetal thymus organ culture with (2 samples for each category) or without (1 sample for each category) RANKL stimulation for 4 days under 2-DG. One µg of total RNA was used for library construction with TruSeq RNA Sample Prep Kit v2. THe ligated products were amplified using 8 cycles of PCR to generate RNA-seq library. Library integrity was verified by Bioanalyzer DNA1000 assay. Sequencing was performed in 101-bp paired-end mode using an Illumina HiSeq.Technical duplicate has done.