Bacterial RNA-seq Research Articles

Improved Bacterial Single-Cell RNA-Seq through Automated MATQ-Seq and Cas9-Based Removal of rRNA Reads.

Bulk RNA sequencing technologies have provided invaluable insights into host and bacterial gene expression and associated regulatory networks. Nevertheless, the majority of these approaches report average expression across cell populations, hiding the true underlying expression patterns that are often heterogeneous in nature. Due to technical advances, single-cell transcriptomics in bacteria has recently become reality, allowing exploration of these heterogeneous populations, which are often the result of environmental changes and stressors. In this work, we have improved our previously published bacterial single-cell RNA sequencing (scRNA-seq) protocol that is based on multiple annealing and deoxycytidine (dC) tailing-based quantitative scRNA-seq (MATQ-seq), achieving a higher throughput through the integration of automation. We also selected a more efficient reverse transcriptase, which led to reduced cell loss and higher workflow robustness. Moreover, we successfully implemented a Cas9-based rRNA depletion protocol into the MATQ-seq workflow. Applying our improved protocol on a large set of single Salmonella cells sampled over different growth conditions revealed improved gene coverage and a higher gene detection limit compared to our original protocol and allowed us to detect the expression of small regulatory RNAs, such as GcvB or CsrB at a single-cell level. In addition, we confirmed previously described phenotypic heterogeneity in Salmonella in regard to expression of pathogenicity-associated genes. Overall, the low percentage of cell loss and high gene detection limit makes the improved MATQ-seq protocol particularly well suited for studies with limited input material, such as analysis of small bacterial populations in host niches or intracellular bacteria. IMPORTANCE Gene expression heterogeneity among isogenic bacteria is linked to clinically relevant scenarios, like biofilm formation and antibiotic tolerance. The recent development of bacterial single-cell RNA sequencing (scRNA-seq) enables the study of cell-to-cell variability in bacterial populations and the mechanisms underlying these phenomena. Here, we report a scRNA-seq workflow based on MATQ-seq with increased robustness, reduced cell loss, and improved transcript capture rate and gene coverage. Use of a more efficient reverse transcriptase and the integration of an rRNA depletion step, which can be adapted to other bacterial single-cell workflows, was instrumental for these improvements. Applying the protocol to the foodborne pathogen Salmonella, we confirmed transcriptional heterogeneity across and within different growth phases and demonstrated that our workflow captures small regulatory RNAs at a single-cell level. Due to low cell loss and high transcript capture rates, this protocol is uniquely suited for experimental settings in which the starting material is limited, such as infected tissues.

S208 Increased Primary Tumor Sutterella Bacterial RNAseq Signatures Are Associated With Increased M2 Macrophage Abundance and Worse Overall Survival in Rectal Adenocarcinoma

S208 Increased Primary Tumor Sutterella Bacterial RNAseq Signatures Are Associated With Increased M2 Macrophage Abundance and Worse Overall Survival in Rectal Adenocarcinoma

Profiling of bacterial transcriptome from ultra-low input with MiniBac-seq.

There is a lack of appropriate methods for preparing bacterial RNA-seq library with ultra-low amount of RNA. To address this issue, we developed miniBac-seq, a strand-specific method for high-quality library construction from sub-nanogram of total RNA, which is 100-fold lower than the current benchmark kit and dramatically reduces preparation cost ($28 + $15 × samples). We further demonstrated the high sensitivity of miniBac-seq via detecting more than 500 genes from amount of total RNA equivalent to that of a single bacterial cell. Finally, we profiled the transcriptome of growth-arrested bacteria in isogenic culture of Escherichia coli. This subpopulation of bacteria is generally low in abundance but is a potent reservoir of antibiotic persistence, and their gene expression has been largely unknown due to technical limitations. Using miniBac-seq, we identified potential molecular driver towards arrested growth as well as antibiotic tolerance. Our method thus expands the capacity to quantify bacterial transcriptome in situ, which is useful to the understanding of bacterial physiology and regulation in their native contexts.

SCORE: Smart Consensus Of RNA Expression-a consensus tool for detecting differentially expressed genes in bacteria.

RNA-sequencing (RNA-Seq) is the current method of choice for studying bacterial transcriptomes. To date, many computational pipelines have been developed to predict differentially expressed genes from RNA-Seq data, but no gold-standard has been widely accepted. We present the Snakemake-based tool Smart Consensus Of RNA Expression (SCORE) which uses a consensus approach founded on a selection of well-established tools for differential gene expression analysis. This allows SCORE to increase the overall prediction accuracy and to merge varying results into a single, human-readable output. SCORE performs all steps for the analysis of bacterial RNA-Seq data, from read preprocessing to the overrepresentation analysis of significantly associated ontologies. Development of consensus approaches like SCORE will help to streamline future RNA-Seq workflows and will fundamentally contribute to the creation of new gold-standards for the analysis of these types of data. https://github.com/SiWolf/SCORE. Supplementary data are available at Bioinformatics online.

Improved bacterial RNA-seq by Cas9-based depletion of ribosomal RNA reads.

A major challenge for RNA-seq analysis of gene expression is to achieve sufficient coverage of informative nonribosomal transcripts. In eukaryotic samples, this is typically achieved by selective oligo(dT)-priming of messenger RNAs to exclude ribosomal RNA (rRNA) during cDNA synthesis. However, this strategy is not compatible with prokaryotes in which functional transcripts are generally not polyadenylated. To overcome this, we adopted DASH (depletion of abundant sequences by hybridization), initially developed for eukaryotic cells, to improve both the sensitivity and depth of bacterial RNA-seq. DASH uses the Cas9 nuclease to remove unwanted cDNA sequences prior to library amplification. We report the design, evaluation, and optimization of DASH experiments for standard bacterial short-read sequencing approaches, including software for automated guide RNA (gRNA) design for Cas9-mediated cleavage in bacterial rDNA sequences. Using these gRNA pools, we effectively removed rRNA reads (56%–86%) in RNA-seq libraries from two different model bacteria, the Gram-negative pathogen Salmonella enterica and the anaerobic gut commensal Bacteroides thetaiotaomicron. DASH works robustly, even with subnanogram amounts of input RNA. Its efficiency, high sensitivity, ease of implementation, and low cost (∼$5 per sample) render DASH an attractive alternative to rRNA removal protocols, in particular for material-constrained studies where conventional ribodepletion techniques fail.

Scalable and cost-effective ribonuclease-based rRNA depletion for transcriptomics.

Bacterial RNA sequencing (RNA-seq) is a powerful approach for quantitatively delineating the global transcriptional profiles of microbes in order to gain deeper understanding of their physiology and function. Cost-effective bacterial RNA-seq requires efficient physical removal of ribosomal RNA (rRNA), which otherwise dominates transcriptomic reads. However, current methods to effectively deplete rRNA of diverse non-model bacterial species are lacking. Here, we describe a probe and ribonuclease based strategy for bacterial rRNA removal. We implemented the method using either chemically synthesized oligonucleotides or amplicon-based single-stranded DNA probes and validated the technique on three novel gut microbiota isolates from three distinct phyla. We further showed that different probe sets can be used on closely related species. We provide a detailed methods protocol, probe sets for >5000 common microbes from RefSeq, and an online tool to generate custom probe libraries. This approach lays the groundwork for large-scale and cost-effective bacterial transcriptomics studies.

Nucleic acid cleavage with a hyperthermophilic Cas9 from an uncultured Ignavibacterium

Clustered regularly interspaced short palindromic repeats (CRISPR)-associated 9 (Cas9) systems have been effectively harnessed to engineer the genomes of organisms from across the tree of life. Nearly all currently characterized Cas9 proteins are derived from mesophilic bacteria, and canonical Cas9 systems are challenged by applications requiring enhanced stability or elevated temperatures. We discovered IgnaviCas9, a Cas9 protein from a hyperthermophilic Ignavibacterium identified through mini-metagenomic sequencing of samples from a hot spring. IgnaviCas9 is active at temperatures up to 100 °C in vitro, which enables DNA cleavage beyond the 44 °C limit of Streptococcus pyogenes Cas9 (SpyCas9) and the 70 °C limit of both Geobacillus stearothermophilus Cas9 (GeoCas9) and Geobacillus thermodenitrificans T12 Cas9 (ThermoCas9). As a potential application of this enzyme, we demonstrate that IgnaviCas9 can be used in bacterial RNA-seq library preparation to remove unwanted cDNA from 16s ribosomal rRNA without increasing the number of steps, thus underscoring the benefits provided by its exceptional thermostability in improving molecular biology and genomic workflows. IgnaviCas9 is an exciting addition to the CRISPR-Cas9 toolbox and expands its temperature range.

APERO: a genome-wide approach for identifying bacterial small RNAs from RNA-Seq data.

Small non-coding RNAs (sRNAs) regulate numerous cellular processes in all domains of life. Several approaches have been developed to identify them from RNA-seq data, which are efficient for eukaryotic sRNAs but remain inaccurate for the longer and highly structured bacterial sRNAs. We present APERO, a new algorithm to detect small transcripts from paired-end bacterial RNA-seq data. In contrast to previous approaches that start from the read coverage distribution, APERO analyzes boundaries of individual sequenced fragments to infer the 5′ and 3′ ends of all transcripts. Since sRNAs are about the same size as individual fragments (50–350 nucleotides), this algorithm provides a significantly higher accuracy and robustness, e.g., with respect to spontaneous internal breaking sites. To demonstrate this improvement, we develop a comparative assessment on datasets from Escherichia coli and Salmonella enterica, based on experimentally validated sRNAs. We also identify the small transcript repertoire of Dickeya dadantii including putative intergenic RNAs, 5′ UTR or 3′ UTR-derived RNA products and antisense RNAs. Comparisons to annotations as well as RACE-PCR experimental data confirm the precision of the detected transcripts. Altogether, APERO outperforms all existing methods in terms of sRNA detection and boundary precision, which is crucial for comprehensive genome annotations. It is freely available as an open source R package on https://github.com/Simon-Leonard/APERO

Global gene expression profiling of a virulent Klebsiella pneumoniae strain during pulmonary infection

Background Klebsiella pneumoniae (Kpn) is an important respiratory pathogen associated with significant mortality, fierce inflammatory responses and high rates of antimicrobial resistance. The increasing incidence of multidrug resistant Kpn has significantly narrowed the therapeutic options available; as such, there is an urgent need to better understand Kpn pathophysiology to identify novel therapeutic targets. Here we performed an in vivo transcriptomic analysis of Kpn isolated from a mammalian host with pulmonary infection. Methods C57BL/6 mice were intranasally inoculated with the virulent Kp52.145 strain (serotype O1:K2); with lungs extracted, homogenised and pooled (n=3; in duplicate) at 32 h post-infection for bacterial RNA purification and RNA-Seq (Illumina). Differential gene expression (analysed using Degust [Voom/Limma]; FDR cut-off =0.01, abs log-FC=2) was assessed in comparison with mid-log phase growth in Lennox broth. Results Overall, we identified >900 differentially expressed genes (DEGs), comprising ∼17 % of the combined chromosomal and plasmid coding sequence repertoire. 52 % of the DEGs were upregulated during infection, including several siderophore-independent iron-, manganese- and zinc-uptake systems (e.g. hmuRSTUV, sitABCD, mntH and znuACB). We also observed a marked in vivo oxidative stress signal, with several Kpn oxidative stress response genes (e.g. oxyR, katE, katG) upregulated during infection. In contrast, expression of mgrB, a negative-regulator of the PhoPQ two-component system associated with resistance to host antimicrobial peptides was downregulated. Conclusion This study provides a novel insight into Kpn gene expression during pulmonary infection. Overall, our data suggest that adaptation to metal starvation, oxidative stress and innate immune defenses are critical for the success of Kpn lung infection.

An undergraduate laboratory on RNA sequencing analysis of bacterial gene expression.

Next generation sequencing has revolutionized molecular biology and has provided a mechanism for rapid DNA and RNA sequence analysis. Yet, there are few resources to introduce next generation sequencing into the undergraduate biochemistry and molecular biology curriculum. Herein, we describe the design, execution, and assessment of a four-week laboratory for junior and senior undergraduate students that focuses on bacterial gene expression changes detected by RNA sequencing (RNA-seq). In the laboratory, students analyze a bacterial RNA-seq dataset in detail and answer questions relating to the impact of DNA methylation on bacterial gene expression. In addition, students confirm key results from the RNA-seq dataset using qRT-PCR and compare their results to similar experiments in the literature. A major strength of the laboratory is the ability of students to analyze raw RNA-seq data. In addition, another strength of the laboratory is the utilization of both dry approaches (informatics and statistics) and wet approaches (RNA isolation, cDNA synthesis, and qRT-PCR) to answer bacterial gene expression questions. Assessment of the laboratory indicates that significant learning gains were achieved with respect to next generation sequencing and RNA-seq. We expect that the laboratory will be a valuable resource as is, or via modification with other datasets and projects. © 2019 International Union of Biochemistry and Molecular Biology, 47(2): 161-167, 2019.

SPARTA: Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis.

BackgroundMany tools exist in the analysis of bacterial RNA sequencing (RNA-seq) transcriptional profiling experiments to identify differentially expressed genes between experimental conditions. Generally, the workflow includes quality control of reads, mapping to a reference, counting transcript abundance, and statistical tests for differentially expressed genes. In spite of the numerous tools developed for each component of an RNA-seq analysis workflow, easy-to-use bacterially oriented workflow applications to combine multiple tools and automate the process are lacking. With many tools to choose from for each step, the task of identifying a specific tool, adapting the input/output options to the specific use-case, and integrating the tools into a coherent analysis pipeline is not a trivial endeavor, particularly for microbiologists with limited bioinformatics experience.ResultsTo make bacterial RNA-seq data analysis more accessible, we developed a Simple Program for Automated reference-based bacterial RNA-seq Transcriptome Analysis (SPARTA). SPARTA is a reference-based bacterial RNA-seq analysis workflow application for single-end Illumina reads. SPARTA is turnkey software that simplifies the process of analyzing RNA-seq data sets, making bacterial RNA-seq analysis a routine process that can be undertaken on a personal computer or in the classroom. The easy-to-install, complete workflow processes whole transcriptome shotgun sequencing data files by trimming reads and removing adapters, mapping reads to a reference, counting gene features, calculating differential gene expression, and, importantly, checking for potential batch effects within the data set. SPARTA outputs quality analysis reports, gene feature counts and differential gene expression tables and scatterplots.ConclusionsSPARTA provides an easy-to-use bacterial RNA-seq transcriptional profiling workflow to identify differentially expressed genes between experimental conditions. This software will enable microbiologists with limited bioinformatics experience to analyze their data and integrate next generation sequencing (NGS) technologies into the classroom. The SPARTA software and tutorial are available at sparta.readthedocs.org.

Orione, a web-based framework for NGS analysis in microbiology

Summary: End-to-end next-generation sequencing microbiology data analysis requires a diversity of tools covering bacterial resequencing, de novo assembly, scaffolding, bacterial RNA-Seq, gene annotation and metagenomics. However, the construction of computational pipelines that use different software packages is difficult owing to a lack of interoperability, reproducibility and transparency. To overcome these limitations we present Orione, a Galaxy-based framework consisting of publicly available research software and specifically designed pipelines to build complex, reproducible workflows for next-generation sequencing microbiology data analysis. Enabling microbiology researchers to conduct their own custom analysis and data manipulation without software installation or programming, Orione provides new opportunities for data-intensive computational analyses in microbiology and metagenomics.Availability and implementation: Orione is available online at http://orione.crs4.it.Contact: gianmauro.cuccuru@crs4.itSupplementary information: Supplementary data are available at Bioinformatics online.

Correcting imbalanced reads coverage in bacterial transcriptome sequencing with extreme deep coverage

High throughput bacterial RNA-Seq experiments can generate extremely high and imbalanced sequencing coverage. Over- or under-estimation of gene expression levels will hinder accurate gene differential expression analysis. Here we evaluated strategies to identify expression differences of genes with high coverage in bacterial transcriptome data using either raw sequence reads or unique reads with duplicate fragments removed. In addition, we proposed a generalised linear model (GLM) based approach to identify imbalance in read coverage based on sequence compositions. Our results show that analysis using raw reads identifies more differentially expressed genes with more accurate fold change than using unique reads. We also demonstrate the presence of sequence composition related biases that are independent of gene expression levels and experimental conditions. Finally, genes that still show strong coverage imbalance after correction were tagged using statistical approach.