RNA-Seq of \u3cem\u3eBorrelia burgdorferi\u3c/em\u3e in Multiple Phases of Growth Reveals Insights into the Dynamics of Gene Expression, Transcriptome Architecture, and Noncoding RNAs

Borrelia burgdorferi, the agent of Lyme disease, differentially expresses numerous genes and proteins as it cycles between mammalian hosts and tick vectors. Insights on regulatory mechanisms have been provided by earlier studies that examined B. burgdorferi gene expression patterns during cultivation. However, prior studies examined bacteria at only a single time point of cultivation, providing only a snapshot of what is likely a dynamic transcriptional program driving B. burgdorferi adaptations to changes during culture growth phases. To address that concern, we performed RNA sequencing (RNA-Seq) analysis of B. burgdorferi cultures at early-exponential, mid-exponential, and early-stationary phases of growth. We found that expression of nearly 18% of annotated B. burgdorferi genes changed significantly during culture maturation. Moreover, genome-wide mapping of the B. burgdorferi transcriptome in different growth phases enabled insight on transcript boundaries, operon structures, and identified numerous putative non-coding RNAs. These RNA-Seq data are discussed and presented as a resource for the community of researchers seeking to better understand B. burgdorferi biology and pathogenesis

Microbial Co-Infection Alters Macrophage Polarization, Phagosomal Escape, and Microbial Killing

Macrophages are important innate immune cells that respond to microbial insults. In response to multi-bacterial infection, the macrophage activation state may change upon exposure to nascent mediators, which results in different bacterial killing mechanism(s). In this study, we utilized two respiratory bacterial pathogens, Mycobacterium bovis (Bacillus Calmette Guẻrin, BCG) and Francisella tularensis live vaccine strain (LVS) with different phagocyte evasion mechanisms, as model microbes to assess the influence of initial bacterial infection on the macrophage response to secondary infection. Non-activated (M0) macrophages or activated M2-polarized cells (J774 cells transfected with the mouse IL-4 gene) were first infected with BCG for 24–48 h, subsequently challenged with LVS, and the results of inhibition of LVS replication in the macrophages was assessed. BCG infection in M0 macrophages activated TLR2-MyD88 and Mincle-CARD9 signaling pathways, stimulating nitric oxide (NO) production and enhanced killing of LVS. BCG infection had little effect on LVS escape from phagosomes into the cytosol in M0 macrophages. In contrast, M2-polarized macrophages exhibited enhanced endosomal acidification, as well as inhibiting LVS replication. Pre-infection with BCG did not induce NO production and thus did not further reduce LVS replication. This study provides a model for studies of the complexity of macrophage activation in response to multi-bacterial infection

Evasion of IFN-γ Signaling by Francisella novicida Is Dependent upon Francisella Outer Membrane Protein C

Francisella tularensis is a Gram-negative facultative intracellular bacterium and the causative agent of the lethal disease tularemia. An outer membrane protein (FTT0918) of F. tularensis subsp. tularensis has been identified as a virulence factor. We generated a F. novicida (F. tularensis subsp. novicida) FTN_0444 (homolog of FTT0918) fopC mutant to study the virulence-associated mechanism(s) of FTT0918.The ΔfopC strain phenotype was characterized using immunological and biochemical assays. Attenuated virulence via the pulmonary route in wildtype C57BL/6 and BALB/c mice, as well as in knockout (KO) mice, including MHC I, MHC II, and µmT (B cell deficient), but not in IFN-γ or IFN-γR KO mice was observed. Primary bone marrow derived macrophages (BMDM) prepared from C57BL/6 mice treated with rIFN-γ exhibited greater inhibition of intracellular ΔfopC than wildtype U112 strain replication; whereas, IFN-γR KO macrophages showed no IFN-γ-dependent inhibition of ΔfopC replication. Moreover, phosphorylation of STAT1 was downregulated by the wildtype strain, but not the fopC mutant, in rIFN-γ treated macrophages. Addition of NG-monomethyl-L-arginine, an NOS inhibitor, led to an increase of ΔfopC replication to that seen in the BMDM unstimulated with rIFN-γ. Enzymatic screening of ΔfopC revealed aberrant acid phosphatase activity and localization. Furthermore, a greater abundance of different proteins in the culture supernatants of ΔfopC than that in the wildtype U112 strain was observed.F. novicida FopC protein facilitates evasion of IFN-γ-mediated immune defense(s) by down-regulation of STAT1 phosphorylation and nitric oxide production, thereby promoting virulence. Additionally, the FopC protein also may play a role in maintaining outer membrane stability (integrity) facilitating the activity and localization of acid phosphatases and other F. novicida cell components

Transcriptomic insights on the virulence-controlling CsrA, BadR, RpoN, and RpoS regulatory networks in the Lyme disease spirochete.

Borrelia burgdorferi, the causative agent of Lyme disease, survives in nature through a cycle that alternates between ticks and vertebrates. To facilitate this defined lifestyle, B. burgdorferi has evolved a gene regulatory network that ensures transmission between those hosts, along with specific adaptations to niches within each host. Several regulatory proteins are known to be essential for the bacterium to complete these critical tasks, but interactions between regulators had not previously been investigated in detail, due to experimental uses of different strain backgrounds and growth conditions. To address that deficit in knowledge, the transcriptomic impacts of four critical regulatory proteins were examined in a uniform strain background. Pairs of mutants and their wild-type parent were grown simultaneously under a single, specific culture condition, permitting direct comparisons between the mutant strains. Transcriptomic analyses were strand-specific, and assayed both coding and noncoding RNAs. Intersection analyses identified regulatory overlaps between regulons, including transcripts involved in carbohydrate and polyamine metabolism. In addition, it was found that transcriptional units such as ospC and dbpBA, which were previously observed to be affected by alternative sigma factors, are transcribed by RNA polymerase using the housekeeping sigma factor, RpoD

RNA-Seq of <i>Borrelia burgdorferi</i> in Multiple Phases of Growth Reveals Insights into the Dynamics of Gene Expression, Transcriptome Architecture, and Noncoding RNAs

<div><p><i>Borrelia burgdorferi</i>, the agent of Lyme disease, differentially expresses numerous genes and proteins as it cycles between mammalian hosts and tick vectors. Insights on regulatory mechanisms have been provided by earlier studies that examined <i>B</i>. <i>burgdorferi</i> gene expression patterns during cultivation. However, prior studies examined bacteria at only a single time point of cultivation, providing only a snapshot of what is likely a dynamic transcriptional program driving <i>B</i>. <i>burgdorferi</i> adaptations to changes during culture growth phases. To address that concern, we performed RNA sequencing (RNA-Seq) analysis of <i>B</i>. <i>burgdorferi</i> cultures at early-exponential, mid-exponential, and early-stationary phases of growth. We found that expression of nearly 18% of annotated <i>B</i>. <i>burgdorferi</i> genes changed significantly during culture maturation. Moreover, genome-wide mapping of the <i>B</i>. <i>burgdorferi</i> transcriptome in different growth phases enabled insight on transcript boundaries, operon structures, and identified numerous putative non-coding RNAs. These RNA-Seq data are discussed and presented as a resource for the community of researchers seeking to better understand <i>B</i>. <i>burgdorferi</i> biology and pathogenesis.</p></div

Volcano plots of differentially expressed genes.

<p>Fold changes between genes were plotted compared to adjusted p-value when comparing (A) early-exponential against mid-exponential, (B) mid-exponential against stationary, and (C) early-exponential against stationary. Criteria of >2X change in expression and <0.05 adjusted p-value were used to define significantly changed genes, and are shown on the plot with the appropriate limiting lines. Genes which met the criteria and were expressed at higher levels in a particular comparison are shown in red and those which were expressed at lower levels are shown in blue.</p

Comparison of predicted transcriptional start sites with previously identified transcriptional start sites.

<p>Comparison of genes with previously identified transcriptional start sites that were also identified by RNA-Seq. Columns list the gene, the previously-mapped start-site location, citation for that determination, and RNA-Seq determined sites during early-exponential, mid-exponential, and stationary phases of growth. For some operons, RNA-Seq algorithms identified different start sites from different cultures; in which case, all called start sites are listed.</p

Examples of Rho-independent termination sites at the end of genes that were bioinformatically predicted and supported by transcriptomic data.

<p><b>(A)</b><i>flaB</i>; and <b>(B)</b><i>bmpDCAB</i>. Thin red lines indicate transcript abundance from the + strand (left to right) and thin blue indicate transcript abundance from the–strand (right to left), and are shown above (+) and below (-) the central axis. Genes are noted below coverage plots and directionality is indicated by arrows at the ends of genes. Relative orientation of genes on the X axis is consistent with RefSeq annotations. Coverage per base is given on the Y axis to the left of the plot. Predicted Rho-independent terminators are indicated by red boxes on the same plane as the gene annotations and directionality is indicated by arrows. Terminators and the associated coverage at their location are highlighted by grey boxes.</p

Examples of intergenic noncoding RNAs.

<p>Red lines indicate transcript abundance from the + strand (left to right) and blue indicate transcript abundance from the–strand (right to left), and are shown above (+) and below (-) the central axis. Genes are noted below coverage plots and directionality is given by arrows at the ends of genes. Relative orientation of genes on the X axis is consistent with RefSeq annotations. Coverage per base is given on the Y axis to the left of the plot. Neither <i>dsrA</i> (E) nor <i>6S</i> (C) are currently annotated in either GenBank or RFAM. <i>dsrA</i> is shown as the longest possible transcript described in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0164165#pone.0164165.ref042" target="_blank">42</a>].</p