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MdaB and NfrA, two novel reductases important in the survival and persistence of the major enteropathogen Campylobacter jejuni

ABSTRACT: Methods: RNA-Seq was used to identify differentially expressed genes between wild type strains, ∆rrpA and ∆rrpB mutants at mid-log (~6 hrs) of growth. 11168H wild type, 11168H∆rrpA, 11168H∆rrpB and 11168H∆rrpAB were plated out on BA plates and incubated at 37°C under microaerobic conditions for 6 hrs. RNA was extracted from the pelleted bacteria cells using PureLink™ RNA Mini Kit (Invitrogen), following manufactures protocol. RNA was isolated from five independent experiments performed on different days Ribosomal RNA was depleted using Ribominus (Invitrogen).Libraries were prepared using TruSeq® Stranded mRNA (Illumina).The paired-end reads were trimmed and filtered using Sickle v1.200 by using a sliding window approach and trimming the reads where the average base quality drops below 20. Only the reads that were above 10 bp length were kept after trimming. Bowtie2 was used to map the reads against the reference sequences; C. jejuni strains 11168H assembly GCA_900117385.1. This generated the mapping in SAM format which were converted to BAM format using Samtools and were index sorted. We used gffread from cufflinks suite to convert annotations from GFF to GTF format. These were used in Bedtools (with multicov-bams option)with the mapped reads to generate transcript counts per samples. A shell utility by the authorswas then used to collate all these transcripts into a n=20 samples x p=1,628 transcripts for 11168H strain. Statistical analysis was performed in R using the combined data generated from the bioinformatics as well as meta data associated with the study (multifactorial design). We used DESeq DataSet from Matrix()function fromDESeq2 package with the adjusted p-value significance cut-off of 0.05 and log fold change cut-off of 1.5. This function uses negative binomial GLM to obtain maximum likelihood estimates for the transcripts log fold change between the two conditions.

ORGANISM(S): Campylobacter jejuni

PROVIDER: GSE174333 | GEO | 2021/09/30

REPOSITORIES: GEO

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Project description:Purpose:Metabolic syndrome (MetS) is associated with a group of conditions including diabetes, obesity, insulin resistance etc. The goal of our study is to identify the differentially regulated genes under metabolic syndromes induced by TNF-α. Methods:A Homosapines Reference based Transcriptome sequencing is performed to understand the genes that are diffrerentially regulated under metabolic synromes with TNF-α as upstream. The HEK293 cells were treated with TNF alpha at 10ng/ml for 6hrs to induce the metabolic disturbances mimicking the metabolic syndromes and untreated HEK 293 cells were considered as control. For the Control set (31 million reads) and TNF-α treated (34 million reads) samples was obtained through the Illumina NextSeq 500 platform and comprised of paired end reads of 76 bp (2X76). FastQC software was used to check the quality of the NextSeq 500 paired end reads and were further processed by in-house script for adapter removal and low quality bases trimming. Tophat-2.02 and Cufflinks-2.1.14 tools were used for the analysis. A two fold change and p-value <0.05 cut-off was used to report the differentially expressed genes (DEGs). Owing to the absence of replicates, no FDR correction was used.For each gene a RPKM (Reads per kilo base per million mapped reads) value was calculated for each gene. Apart from the RPKM value, p-value and a fold change value was also calculated for control and treated samples. The genes were validated using qPCR using using Brilliant Ш Ultra-Fast SYBR green qPCR Master Mix in AriaMx PCR System (Agilent Technologies). Results: Around 6056 and 5495 transcripts were shown as up and down regulated respectively using a twofold change cut-off. On excluding the transcripts with no gene names, the numbers of up and down regulated transcripts were found out to be 4444 (3551 genes) and 4365 (3418 genes) respectively.Among which 1218 genes are unique, and highly significant(based on p-value cut-off) and transcriptionally regulated genes by TNF-α, among which about 15% are RBPs; identified by the EuRBPDB data base. Among these transcriptionally regulated genes, we have identified 204 MetS genes by IPA analysis, more than 10% of which are identified as RBPs. Conclusion: Transcriptional regulation of the MetS genes by TNF-α, which also act as RBPs predicts the existence of feed-forward loops and interconnected pathways, which may influence the onset of MetS at the molecular level.

Project description:Background High-throughput RNA-Sequencing (RNA-Seq) has become the preferred technique for studying gene expression differences between biological samples and for discovering novel isoforms, though the techniques to analyze the resulting data are still immature. One pre-processing step that is widely but heterogeneously applied is trimming, in which low quality bases, identified by the probability that they are called incorrectly, are removed. However, the impact of trimming on subsequent alignment to a genome could influence downstream analyses including gene expression estimation; we hypothesized that this might occur in an inconsistent manner across different genes, resulting in differential bias. Results To assess the effects of trimming on gene expression, we generated RNA-Seq data sets from four samples of larval Drosophila melanogaster sensory neurons, and used three trimming algorithms—SolexaQA, Trimmomatic, and ConDeTri—to perform quality-based trimming across a wide range of stringencies. After aligning the reads to the D. melanogaster genome with TopHat2, we used Cuffdiff2 to compare the original, untrimmed gene expression estimates to those following trimming. With the most aggressive trimming parameters, over ten percent of genes had significant changes in their estimated expression levels. This trend was seen with two additional RNA-Seq data sets and with alternative differential expression analysis pipelines. We found that the majority of the expression changes could be mitigated by imposing a minimum length filter following trimming, suggesting that the differential gene expression was primarily being driven by spurious mapping of short reads. Slight differences with the untrimmed data set remained after length filtering, which were associated with genes with low exon numbers and high GC content. Finally, an analysis of paired RNA-seq/microarray data sets suggests that no or modest trimming results in the most biologically accurate gene expression estimates. Conclusions We find that aggressive quality-based trimming has a large impact on the apparent makeup of RNA-Seq-based gene expression estimates, and that short reads can have a particularly strong impact. We conclude that implementation of trimming in RNA-Seq analysis workflows warrants caution, and if used, should be used in conjunction with a minimum read length filter to minimize the introduction of unpredictable changes in expression estimates.

Project description:Background High-throughput RNA-Sequencing (RNA-Seq) has become the preferred technique for studying gene expression differences between biological samples and for discovering novel isoforms, though the techniques to analyze the resulting data are still immature. One pre-processing step that is widely but heterogeneously applied is trimming, in which low quality bases, identified by the probability that they are called incorrectly, are removed. However, the impact of trimming on subsequent alignment to a genome could influence downstream analyses including gene expression estimation; we hypothesized that this might occur in an inconsistent manner across different genes, resulting in differential bias. Results To assess the effects of trimming on gene expression, we generated RNA-Seq data sets from four samples of larval Drosophila melanogaster sensory neurons, and used three trimming algorithmsâSolexaQA, Trimmomatic, and ConDeTriâto perform quality-based trimming across a wide range of stringencies. After aligning the reads to the D. melanogaster genome with TopHat2, we used Cuffdiff2 to compare the original, untrimmed gene expression estimates to those following trimming. With the most aggressive trimming parameters, over ten percent of genes had significant changes in their estimated expression levels. This trend was seen with two additional RNA-Seq data sets and with alternative differential expression analysis pipelines. We found that the majority of the expression changes could be mitigated by imposing a minimum length filter following trimming, suggesting that the differential gene expression was primarily being driven by spurious mapping of short reads. Slight differences with the untrimmed data set remained after length filtering, which were associated with genes with low exon numbers and high GC content. Finally, an analysis of paired RNA-seq/microarray data sets suggests that no or modest trimming results in the most biologically accurate gene expression estimates. Conclusions We find that aggressive quality-based trimming has a large impact on the apparent makeup of RNA-Seq-based gene expression estimates, and that short reads can have a particularly strong impact. We conclude that implementation of trimming in RNA-Seq analysis workflows warrants caution, and if used, should be used in conjunction with a minimum read length filter to minimize the introduction of unpredictable changes in expression estimates. Four biological replicates of 100 Drosophila melanogaster larval multi-dendritic sensory neurons were profiled by mRNA-Seq

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MdaB and NfrA, two novel reductases important in the survival and persistence of the major enteropathogen Campylobacter jejuni

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