Use of a Capture-Based Pathogen Transcript Enrichment Strategy for RNA-Seq Analysis of the <i>Francisella Tularensis</i> LVS Transcriptome during Infection of Murine Macrophages

<div><p><i>Francisella tularensis</i> is a zoonotic intracellular pathogen that is capable of causing potentially fatal human infections. Like all successful bacterial pathogens, <i>F. tularensis</i> rapidly responds to changes in its environment during infection of host cells, and upon encountering different microenvironments within those cells. This ability to appropriately respond to the challenges of infection requires rapid and global shifts in gene expression patterns. In this study, we use a novel pathogen transcript enrichment strategy and whole transcriptome sequencing (RNA-Seq) to perform a detailed characterization of the rapid and global shifts in <i>F. tularensis</i> LVS gene expression during infection of murine macrophages. We performed differential gene expression analysis on all bacterial genes at two key stages of infection: phagosomal escape, and cytosolic replication. By comparing the <i>F. tularensis</i> transcriptome at these two stages of infection to that of the bacteria grown in culture, we were able to identify sets of genes that are differentially expressed over the course of infection. This analysis revealed the temporally dynamic expression of a number of known and putative transcriptional regulators and virulence factors, providing insight into their role during infection. In addition, we identified several <i>F. tularensis</i> genes that are significantly up-regulated during infection but had not been previously identified as virulence factors. These unknown genes may make attractive therapeutic or vaccine targets.</p> </div

Heat map of virulence factor genes up- and down-regulated at each time point.

<p>Change in expression was determined for previously identified <i>F</i>. <i>tularensis</i> virulence factor genes at both post-infection time points, and then clustered to identify genes that are coordinately regulated. The cluster analysis segregated the genes into three groups. Cluster 1, in which the genes are up-regulated at both post-infection time points, is comprised entirely of genes in the FPI.</p

Comparison of the genes up- and down-regulated at each time point.

<p>The Venn diagrams depict the number of genes with significant changes in expression at both the 4 and 8-hour post-infection time points, with the number in the middle representing genes up- or down-regulated at both time points. A) Up-regulated genes. B) Down-regulated genes.</p

Differentially expressed genes plotted across the <i>F. tularensis</i> genome.

<p>All genes that had at least a 4-fold change in expression at either 4 hours (red) or 8 hours (blue) were plotted according to their gene ID number across the genome. The two copies of the FPI are highlighted in the up-regulated portion of the figure, and the ribosomal proteins and ATP synthase subunits are highlighted in the down-regulated portion of the figure.</p

World-to-Digital-Microfluidic Interface Enabling Extraction and Purification of RNA from Human Whole Blood

Digital microfluidics (DMF) is a powerful technique for simple and precise manipulation of microscale droplets of fluid. This technique enables processing and analysis of a wide variety of samples and reagents and has proven useful in a broad range of chemical, biological, and medical applications. Handling of “real-world” samples has been a challenge, however, because typically their volumes are greater than those easily accommodated by DMF devices and contain analytes of interest at low concentration. To address this challenge, we have developed a novel “world-to-DMF” interface in which an integrated companion module drives the large-volume sample through a 10 μL droplet region on the DMF device, enabling magnet-mediated recovery of bead-bound analytes onto the device as they pass through the region. To demonstrate its utility, we use this system for extraction of RNA from human whole blood lysates (110–380 μL) and further purification in microscale volumes (5–15 μL) on the DMF device itself. Processing by the system was >2-fold faster and consumed 12-fold less reagents, yet produced RNA yields and quality fully comparable to conventional preparations and supporting qRT-PCR and RNA-Seq analyses. The world-to-DMF system is designed for flexibility in accommodating different sample types and volumes, as well as for facile integration of additional modules to enable execution of more complex protocols for sample processing and analysis. As the first technology of its kind, this innovation represents an important step forward for DMF, further enhancing its utility for a wide range of applications

Functional schematic of the RZTC wheel.

<p>A fixed tube or capillary containing the sample to be cycled is held against temperature-controlled heater blocks, which are sequentially rotated into contact with the tube to produce the desired temperature cycling. Blocks are arranged around the wheel in order of increasing temperature. The tube rests in a groove on the outer surface of the heater block (right) and is tensioned against the block to maximize thermal coupling and sample ramp rate.</p

4-block RZTC temperature history from startup through a series of four PowerPlex 16 HS runs (shaded) with unloading, cleaning, and loading operations between them.

<p>Block 3 is switched in real time between 96°C hot start and 90°C cycling set-points.</p

Comparison of average thermocouple-measured rise and fall times derived from the five cycles depicted in Fig. 7 with a parametrically fitted bounded exponential curve of the form of Equation 1, and response curves predicted analytically a priori from Equations 1, 2, and 4.

<p>Comparison of average thermocouple-measured rise and fall times derived from the five cycles depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118182#pone.0118182.g007" target="_blank">Fig. 7</a> with a parametrically fitted bounded exponential curve of the form of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118182#pone.0118182.e001" target="_blank">Equation 1</a>, and response curves predicted analytically a priori from Equations <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118182#pone.0118182.e001" target="_blank">1</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118182#pone.0118182.e002" target="_blank">2</a>, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118182#pone.0118182.e004" target="_blank">4</a>.</p

E-Gel image showing alternating positive control and no template control 26-cycle GAPDH / Phusion experiments.

<p>Lanes (M) 50 bp ladder, (1) Bench-top NTC, (2) Bench-top positive control, (3, 5, 7, 9) rzPCR NTC, (4, 6, 8, 10) rzPCR positive control. The yellow arrow indicates the position of primer bands, while green arrows indicate faint nonspecific bands in the rzPCR positive control lanes. Streaks and smudging are post-separation handling artifacts.</p

rzPCR system hardware.

<p>(A) Detail of the sample inlet and reagent distribution portion of the rzPCR system. Sample is aspirated into the capillary shown in the foreground, while the capillary in the back will deliver PCR reagents to a DMF platform in future iterations of the system. Reagent, flushing, and waste reservoirs plumbed to the central multiport valve can be readily emptied or refilled as needed. (B) Four-channel heater control box.</p