Replicates, Read Numbers, and Other Important Experimental Design Considerations for Microbial RNA-seq Identified Using Bacillus thuringiensis Datasets

Pseudomonas virulence factor controls expression of virulence genes in Pseudomonas entomophila

Quorum sensing is a communication strategy that bacteria use to collectively alter gene expression in response to cell density. Pathogens use quorum sensing systems to control activities vital to infection, such as the production of virulence factors and biofilm formation. The Pseudomonas virulence factor (pvf) gene cluster encodes a signaling system (Pvf) that is present in over 500 strains of proteobacteria, including strains that infect a variety of plant and human hosts. We have shown that Pvf regulates the production of secreted proteins and small molecules in the insect pathogen Pseudomonas entomophila L48. Here, we identified genes that are likely regulated by Pvf using the model strain P. entomophila L48 which does not contain other known quorum sensing systems. Pvf regulated genes were identified through comparing the transcriptomes of wildtype P. entomophila and a pvf deletion mutant (ΔpvfA-D). We found that deletion of pvfA-D affected the expression of approximately 300 genes involved in virulence, the type VI secretion system, siderophore transport, and branched chain amino acid biosynthesis. Additionally, we identified seven putative biosynthetic gene clusters with reduced expression in ΔpvfA-D. Our results indicate that Pvf controls multiple virulence mechanisms in P. entomophila L48. Characterizing genes regulated by Pvf will aid understanding of host-pathogen interactions and development of anti-virulence strategies against P. entomophila and other pvf-containing strains.

Robust principal component analysis for accurate outlier sample detection in RNA-Seq data

BACKGROUND

High throughput RNA sequencing is a powerful approach to study gene expression. Due to the complex multiple-steps protocols in data acquisition, extreme deviation of a sample from samples of the same treatment group may occur due to technical variation or true biological differences. The high-dimensionality of the data with few biological replicates make it challenging to accurately detect those samples, and this issue is not well studied in the literature currently. Robust statistics is a family of theories and techniques aim to detect the outliers by first fitting the majority of the data and then flagging data points that deviate from it. Robust statistics have been widely used in multivariate data analysis for outlier detection in chemometrics and engineering. Here we apply robust statistics on RNA-seq data analysis.

RESULTS

We report the use of two robust principal component analysis (rPCA) methods, PcaHubert and PcaGrid, to detect outlier samples in multiple simulated and real biological RNA-seq data sets with positive control outlier samples. PcaGrid achieved 100% sensitivity and 100% specificity in all the tests using positive control outliers with varying degrees of divergence. We applied rPCA methods and classical principal component analysis (cPCA) on an RNA-Seq data set profiling gene expression of the external granule layer in the cerebellum of control and conditional SnoN knockout mice. Both rPCA methods detected the same two outlier samples but cPCA failed to detect any. We performed differentially expressed gene detection before and after outlier removal as well as with and without batch effect modeling. We validated gene expression changes using quantitative reverse transcription PCR and used the result as reference to compare the performance of eight different data analysis strategies. Removing outliers without batch effect modeling performed the best in term of detecting biologically relevant differentially expressed genes.

CONCLUSIONS

rPCA implemented in the PcaGrid function is an accurate and objective method to detect outlier samples. It is well suited for high-dimensional data with small sample sizes like RNA-seq data. Outlier removal can significantly improve the performance of differential gene detection and downstream functional analysis.

Insight into the Lifestyle of Amoeba Willaertia magna during Bioreactor Growth Using Transcriptomics and Proteomics

Willaertia magna C2c maky is a thermophilic free-living amoeba strain that showed ability to eliminate Legionella pneumophila, a pathogenic bacterium living in the aquatic environment. The amoeba industry has proposed the use of Willaertia magna as a natural biocide to control L. pneumophila proliferation in cooling towers. Here, transcriptomic and proteomic studies were carried out in order to expand knowledge on W. magna produced in a bioreactor. Illumina RNA-seq generated 217 million raw reads. A total of 8790 transcripts were identified, of which 6179 and 5341 were assigned a function through comparisons with National Center of Biotechnology Information (NCBI) reference sequence and the Clusters of Orthologous Groups of proteins (COG) databases, respectively. To corroborate these transcriptomic data, we analyzed the W. magna proteome using LC–MS/MS. A total of 3561 proteins were identified. The results of transcriptome and proteome analyses were highly congruent. Metabolism study showed that W. magna preferentially consumed carbohydrates and fatty acids to grow. Finally, an in-depth analysis has shown that W. magna produces several enzymes that are probably involved in the metabolism of secondary metabolites. Overall, our multi-omic study of W. magna opens the way to a better understanding of the genetics and biology of this amoeba.

Bacterial Differential Expression Analysis Methods

RNA-Seq examines global gene expression to provide insights into cellular processes, and it can be particularly informative when comparing contrasting physiological states or strains. Although relatively routine in many laboratories, there are many steps involved in performing a transcriptomics experiment to ensure representative and high-quality results are generated for analysis. In this chapter, we present the application of widely used bioinformatic methodologies to assess, trim, and filter RNA-seq reads for quality using FastQC and Trim Galore, respectively. High-quality reads are mapped using Bowtie2 and differentially expressed genes across different groups were estimated using the DEseq2 R-Bioconductor package. In addition, we describe the various steps to perform the sample-wise data quality assessment by generating exploratory plots through the DESeq2 package. Simple steps to calculate the significant differentially expressed genes, up- and down-regulated genes, and exporting the data and images are also included. A Venn diagram is a useful method to compare the differentially expressed genes across various comparisons and steps to generate the Venn diagram from DESeq2 results are provided. Finally, the output from DESeq2 is compared to published results from EdgeR. The Clostridium autoethanogenum data are published and publicly available.

Transcriptome analysis reveals a high aerobic capacity in the whale brain

The brain of diving mammals is repeatedly exposed to low oxygen conditions (hypoxia) that would have caused severe damage to most terrestrial mammals. Some whales may dive for >2 h with their brain remaining active. Many of the physiological adaptations of whales to diving have been investigated, but little is known about the molecular mechanisms that enable their brain to survive sometimes prolonged periods of hypoxia. Here, we have used an RNA-Seq approach to compare the mRNA levels in the brains of whales with those of cattle, which serves as a terrestrial relative. We sequenced the transcriptomes of the brains from cattle (Bos taurus), killer whale (Orcinus orca), and long-finned pilot whale (Globicephala melas). Further, the brain transcriptomes of cattle, minke whale (Balaenoptera acutorostrata) and bowhead whale (Balaena mysticetus), which were available in the databases, were included. We found a high expression of genes related to oxidative phosphorylation and the respiratory electron chain in the whale brains. In the visual cortex of whales, transcripts related to the detoxification of reactive oxygen species were more highly expressed than in the visual cortex of cattle. These findings indicate a high oxidative capacity in the whale brain that might help to maintain aerobic metabolism in periods of reduced oxygen availability during dives.

High Throughput Sequencing-Based Approaches for Gene Expression Analysis

Next-generation sequencing has emerged as the method of choice to answer fundamental questions in biology. The massively parallel sequencing technology for RNA-Seq analysis enables better understanding of gene expression patterns in model and nonmodel organisms. Sequencing per se has reached the stage of commodity level while analyzing and interpreting huge amount of data has been a significant challenge. This chapter is aimed at discussing the complexities involved in sequencing and analysis, and tries to simplify sequencing based gene expression analysis. Biologists and experimental scientists were kept in mind while discussing the methods and analysis workflow.

Transcriptomic and proteomic changes from medium supplementation and strain evolution in high-yielding Clostridium thermocellum strains

Clostridium thermocellum is a potentially useful organism for the production of lignocellulosic biofuels because of its ability to directly deconstruct cellulose and convert it into ethanol. Previously engineered C. thermocellum strains have achieved higher yields and titers of ethanol. These strains often initially grow more poorly than the wild type. Adaptive laboratory evolution and medium supplementation have been used to improve growth, but the mechanism(s) by which growth improves remain(s) unclear. Here, we studied (1) wild-type C. thermocellum, (2) the slow-growing and high-ethanol-yielding mutant AG553, and (3) the faster-growing evolved mutant AG601, each grown with and without added formate. We used a combination of transcriptomics and proteomics to understand the physiological impact of the metabolic engineering, evolution, and medium supplementation. Medium supplementation with formate improved growth in both AG553 and AG601. Expression of C1 metabolism genes varied with formate addition, supporting the hypothesis that the primary benefit of added formate is the supply of C1 units for biosynthesis. Expression of stress response genes such as those involved in the sporulation cascade was dramatically over-represented in AG553, even after the addition of formate, suggesting that the source of the stress may be other issues such as redox imbalances. The sporulation response is absent in evolved strain AG601, suggesting that sporulation limits the growth of engineered strain AG553. A better understanding of the stress response and mechanisms of improved growth hold promise for informing rational improvement of C. thermocellum for lignocellulosic biofuel production.

Deep sequencing approaches for the analysis of prokaryotic transcriptional boundaries and dynamics

The identification of the protein-coding regions of a genome is straightforward due to the universality of start and stop codons. However, the boundaries of the transcribed regions, conditional operon structures, non-coding RNAs and the dynamics of transcription, such as pausing of elongation, are non-trivial to identify, even in the comparatively simple genomes of prokaryotes. Traditional methods for the study of these areas, such as tiling arrays, are noisy, labour-intensive and lack the resolution required for densely-packed bacterial genomes. Recently, deep sequencing has become increasingly popular for the study of the transcriptome due to its lower costs, higher accuracy and single nucleotide resolution. These methods have revolutionised our understanding of prokaryotic transcriptional dynamics. Here, we review the deep sequencing and data analysis techniques that are available for the study of transcription in prokaryotes, and discuss the bioinformatic considerations of these analyses.