Interaction between the microbiome and TP53 in human lung cancer.

BACKGROUND: Lung cancer is the leading cancer diagnosis worldwide and the number one cause of cancer deaths. Exposure to cigarette smoke, the primary risk factor in lung cancer, reduces epithelial barrier integrity and increases susceptibility to infections. Herein, we hypothesize that somatic mutations together with cigarette smoke generate a dysbiotic microbiota that is associated with lung carcinogenesis. Using lung tissue from 33 controls and 143 cancer cases, we conduct 16S ribosomal RNA (rRNA) bacterial gene sequencing, with RNA-sequencing data from lung cancer cases in The Cancer Genome Atlas serving as the validation cohort. RESULTS: Overall, we demonstrate a lower alpha diversity in normal lung as compared to non-tumor adjacent or tumor tissue. In squamous cell carcinoma specifically, a separate group of taxa are identified, in which Acidovorax is enriched in smokers. Acidovorax temporans is identified within tumor sections by fluorescent in situ hybridization and confirmed by two separate 16S rRNA strategies. Further, these taxa, including Acidovorax, exhibit higher abundance among the subset of squamous cell carcinoma cases with TP53 mutations, an association not seen in adenocarcinomas. CONCLUSIONS: The results of this comprehensive study show both microbiome-gene and microbiome-exposure interactions in squamous cell carcinoma lung cancer tissue. Specifically, tumors harboring TP53 mutations, which can impair epithelial function, have a unique bacterial consortium that is higher in relative abundance in smoking-associated tumors of this type. Given the significant need for clinical diagnostic tools in lung cancer, this study may provide novel biomarkers for early detection

Superhelical Duplex Destabilization and the Recombination Position Effect

The susceptibility to recombination of a plasmid inserted into a chromosome varies with its genomic position. This recombination position effect is known to correlate with the average G+C content of the flanking sequences. Here we propose that this effect could be mediated by changes in the susceptibility to superhelical duplex destabilization that would occur. We use standard nonparametric statistical tests, regression analysis and principal component analysis to identify statistically significant differences in the destabilization profiles calculated for the plasmid in different contexts, and correlate the results with their measured recombination rates. We show that the flanking sequences significantly affect the free energy of denaturation at specific sites interior to the plasmid. These changes correlate well with experimentally measured variations of the recombination rates within the plasmid. This correlation of recombination rate with superhelical destabilization properties of the inserted plasmid DNA is stronger than that with average G+C content of the flanking sequences. This model suggests a possible mechanism by which flanking sequence base composition, which is not itself a context-dependent attribute, can affect recombination rates at positions within the plasmid

Transcriptome-Wide Analysis of Hepatitis B Virus-Mediated Changes to Normal Hepatocyte Gene Expression

<div><p>Globally, a chronic hepatitis B virus (HBV) infection remains the leading cause of primary liver cancer. The mechanisms leading to the development of HBV-associated liver cancer remain incompletely understood. In part, this is because studies have been limited by the lack of effective model systems that are both readily available and mimic the cellular environment of a normal hepatocyte. Additionally, many studies have focused on single, specific factors or pathways that may be affected by HBV, without addressing cell physiology as a whole. Here, we apply RNA-seq technology to investigate transcriptome-wide, HBV-mediated changes in gene expression to identify single factors and pathways as well as networks of genes and pathways that are affected in the context of HBV replication. Importantly, these studies were conducted in an <i>ex vivo</i> model of cultured primary hepatocytes, allowing for the transcriptomic characterization of this model system and an investigation of early HBV-mediated effects in a biologically relevant context. We analyzed differential gene expression within the context of time-mediated gene-expression changes and show that in the context of HBV replication a number of genes and cellular pathways are altered, including those associated with metabolism, cell cycle regulation, and lipid biosynthesis. Multiple analysis pipelines, as well as qRT-PCR and an independent, replicate RNA-seq analysis, were used to identify and confirm differentially expressed genes. HBV-mediated alterations to the transcriptome that we identified likely represent early changes to hepatocytes following an HBV infection, suggesting potential targets for early therapeutic intervention. Overall, these studies have produced a valuable resource that can be used to expand our understanding of the complex network of host-virus interactions and the impact of HBV-mediated changes to normal hepatocyte physiology on viral replication.</p></div

Description of HBV-only subset of differentially expressed genes.

<p>Gene name, Ensembl ID, and associated protein name are given for genes within the HBV-only subset of genes. Fold changes comparing expression in AdGFP to AdGFP-HBV at the indicated time points is included, along with a heatmap depicting expression variation (by Z-score) across all samples.</p

HBV-mediated pathway perturbation.

<p>HBV-mediated pathway perturbation.</p

Confirmation of differentially expressed genes by qRT-PCR.

<p>Expression of genes from the "HBV-only" and "HBV-specific" was confirmed by qRT-PCR (bars labeled qPCR). Expression is presented as fold change of AdGFP-HBV-infected cells compared to AdGFP-infected cells at the indicated time point. Fold change using the RPKM values described in the primary RNA-seq dataset was included for comparison of expression patterns (bars labeled RPKM).</p

Overall analysis of HBV-mediated changes in gene expression.

<p><b>A.</b> Venn diagram of DEG from AdGFP 48hr to AdGFP-HBV 48hr (blue), AdGFP 72hr to AdGFP-HBV 72hr (yellow), AdGFP 48hr to AdGFP 72hr (green) and AdGFP-HBV 48hr to AdGFP-HBV 72hr (red) comparisons. The "HBV-specific" subset (described in text) is circled. <b>B.</b> Venn diagram of DEG from AdGFP 48hr to AdGFP-HBV 48hr (blue), AdGFP 72hr to AdGFP-HBV 72hr (yellow), and any DEG from all Uninfected/time-mediated comparison (green). The "HBV-only" subset is circled. <b>C-D.</b> HBV-mediated gene-expression changes were plotted for 48hr samples (C.) and 72hr samples (D.) as the log₁₀ RPKM value for genes in AdGFP samples versus the log₂ fold change compared to AdGFP for genes in the indicated comparison. Yellow diamonds indicate DEG in the "HBV-only" subset, red squares represent DEG in the "HBV-specific" subset, and blue circles represent all DEG in the AdGFP to AdGFP-HBV comparison at the indicated time point.</p

HBV-mediated impact on time-mediated, differential-gene expression.

<p><b>A-H.</b> Individual gene expression was plotted as RPKM value over time for uninfected PRHs (black), AdGFP-infected PRHs (red), and AdGFP-HBV-infected PRHs (blue).</p

Confirmation of experimental system.

<p><b>A</b>. Experimental setup for primary dataset. <b>B</b>. PRHs were infected with either AdGFP or AdGFP-HBV, and infection efficiency was determined by monitoring GFP expression at 48hr and 72hr (24hr and 48hr post-infection). <b>C.</b> HBV replication was monitored by Southern blot analysis of HBV core particle-associated DNA. Blot was exposed for 1 day (upper panel) or 7 days (lower panel) to allow visualization of HBV replication at both 48hr and 72hr. RC–relaxed circular DNA, DL–double-stranded linear DNA, SS–single stranded DNA.</p

Differential-gene expression due to time in culture.

<p><b>A.</b> The top 100 most variable genes across all samples were visualized by heatmap. Gene-expression variation was calculated by Z-score, with red indicating an increase in expression compared to the mean across all samples, and blue indicating a decrease in expression. Uninfected 0hr samples were removed from the analysis to prevent a heavy bias due to the large changes within the first 24hr. <b>B.</b> Gene-expression changes in uninfected PRHs over time were plotted as the log₁₀ RPKM value for genes in uninfected 0hr samples versus the log₂ fold change compared to uninfected 0hr for genes in the indicated comparison. Yellow diamonds indicate DEG that were only differentially expressed in the first 24hr, red squares represent DEG at 24hr and 48hr but not 72hr, and blue circles represent DEG at 24hr, 48hr, and 72hr.</p