Comparison of unique sequences and subsequences for RPMI 8226.

<p>Venn diagrams showing the difference between the number of unique 32-base long subsequences (left) and 80-base long sequences (right) from leukemia RPMI 8226 cell line sample that mapped to (with perfect match) and shared by three reference P. <i>acnes</i> genomes (HL096PA1, SK137, and Type IA2 P.acn17).</p

Secondary Analysis of the NCI-60 Whole Exome Sequencing Data Indicates Significant Presence of <i>Propionibacterium acnes</i> Genomic Material in Leukemia (RPMI-8226) and Central Nervous System (SF-295, SF-539, and SNB-19) Cell Lines

<div><p>The NCI-60 human tumor cell line panel has been used in a broad range of cancer research over the last two decades. A landmark 2013 whole exome sequencing study of this panel added an exceptional new resource for cancer biologists. The complementary analysis of the sequencing data produced by this study suggests the presence of <i>Propionibacterium acnes</i> genomic sequences in almost half of the datasets, with the highest abundance in the leukemia (RPMI-8226) and central nervous system (SF-295, SF-539, and SNB-19) cell lines. While the origin of these contaminating bacterial sequences remains to be determined, observed results suggest that computational control for the presence of microbial genomic material is a necessary step in the analysis of the high throughput sequencing (HTS) data.</p></div

Percent of P. <i>acnes</i> HL096PA1 genes identified.

<p>The percent of genes identified out of 2,175 for P. <i>acnes</i> HL096PA1 (NC_021085.1) across all 61 NCI-60 datasets. A gene was considered identified if one or more reads (32-base long subsequences) mapped with no genomic variation (zero mismatch).</p

The relationship of mutation rates and genome size or AT content.

<p>Mutation rate (as X×10⁻⁹ per nucleotide) per generation and mutation rate per cell division are plotted as a function of genome size (in MB) (A and B) and as a function of AT content (C and D). We observed a significant relationship between mutation rate and genome size (Log₁₀(mutation rate) = −1.0909+0.7505×log₁₀(Genome size), with R² = 0.58 and p = 0.0024.), but not for mutation rate per cell division or mutation rates and AT content. <i>D. discoideum</i> is represented by the red dot; the error bars indicate the Poisson confidence interval for our nuclear mutation rate. Mutation rates for Eubacteria and Archaea are given as averages of multiple estimates and are represented by open symbols. The average mutation rate of Eubacteria does not include <i>Buchnera aphidicola</i> due to its unusually high mutation rate, which is characteristic for endosymbionts. Circles represent mutation rate estimates obtained from high-throughput sequencing of MA lines; estimates obtained through other methods are represented by triangles. Mutation rates for yeast are calculated as the average from Lynch <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046759#pone.0046759-Lynch1" target="_blank">[8]</a> and Nishant et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046759#pone.0046759-Nishant1" target="_blank">[15]</a>; all other estimates are from Lynch <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0046759#pone.0046759-Lynch1" target="_blank">[8]</a>.</p

Identification of unchanged sites and unique mutations.

<p>Analysis pipeline and examples of rules used to identify mutations (red) unique to one mutation accumulation line, and unchanged nucleotides (green). Each line represents a hypothetical position in the genome and is characterized by the chromosome (Chr), position (Pos), and the reference base (Ref). Columns 4–6 list the majority consensus base for the three MA lines (MA31, MA47, MA55). Columns 7–9 show the number of reads covering this particular position in the three MA lines, columns 10–12 list the fraction of reads agreeing with the majority base. We used a minimum agreement of 90%. Column 13 gives the read coverage for this position for the self-mapping of the genome and indicates whether the position is uniquely mappable (if self-mapping coverage = 62). Columns 14–16 give the results from our filtering criteria, with unchanged sites indicated in green, mutations in red, and confirming bases in yellow. Positions that were not covered in all three lines were excluded from the analysis.</p

Average windowed nucleotide coverage for P. <i>acnes</i> HL096PA1.

<p>Average window-by-window nucleotide coverage (window size = 250 bases) of the P. <i>acnes</i> HL096PA1 (NC_021085.1) genome by 32-base long subsequences acquired from leukemia (RPMI-8226) and central nervous system (SF-295, SF-539, and SNB-19) sequencing datasets. The average nucleotide coverage per window is equal to the total number of reads that map (cover) each position within the given window divided by the window size (250).</p

Mycoplasma CG- and GATC-specific DNA methyltransferases selectively and efficiently methylate the host genome and alter the epigenetic landscape in human cells

<div><p>Aberrant DNA methylation is frequently observed in disease, including many cancer types, yet the underlying mechanisms remain unclear. Because germline and somatic mutations in the genes that are responsible for DNA methylation are infrequent in malignancies, additional mechanisms must be considered. <i>Mycoplasmas spp</i>., including <i>Mycoplasma hyorhinis,</i> efficiently colonize human cells and may serve as a vehicle for delivery of enzymatically active microbial proteins into the intracellular milieu. Here, we performed, for the first time, genome-wide and individual gene mapping of methylation marks generated by the <i>M. hyorhinis</i> CG- and GATC-specific DNA cytosine methyltransferases (MTases) in human cells. Our results demonstrated that, upon expression in human cells, MTases readily translocated to the cell nucleus. In the nucleus, MTases selectively and efficiently methylated the host genome at the DNA sequence sites free from pre-existing endogenous methylation, including those in a variety of cancer-associated genes. We also established that mycoplasma is widespread in colorectal cancers, suggesting that either the infection contributed to malignancy onset or, alternatively, that tumors provide a favorable environment for mycoplasma growth. In the human genome, ∼11% of GATC sites overlap with CGs (e.g., C<u>GAT^mC</u>G); therefore, the methylated status of these sites can be perpetuated by human DNMT1. Based on these results, we now suggest that the GATC-specific methylation represents a novel type of infection-specific epigenetic mark that originates in human cells with a previous exposure to infection. Overall, our findings unveil an entirely new panorama of interactions between the human microbiome and epigenome with a potential impact in disease etiology.</p></div

Nasopharyngeal microbiota in infants and changes during viral upper respiratory tract infection and acute otitis media

<div><p>Background</p><p>Interferences between pathogenic bacteria and specific commensals are known. We determined the interactions between nasopharyngeal microbial pathogens and commensals during viral upper respiratory tract infection (URI) and acute otitis media (AOM) in infants.</p><p>Methods</p><p>We analyzed 971 specimens collected monthly and during URI and AOM episodes from 139 infants. The 16S rRNA V4 gene regions were sequenced on the Illumina MiSeq platform.</p><p>Results</p><p>Among the high abundant genus-level nasopharyngeal microbiota were <i>Moraxella</i>, <i>Haemophilus</i>, and <i>Streptococcus</i> (3 otopathogen genera), <i>Corynebacterium</i>, <i>Dolosigranulum</i>, <i>Staphylococcus</i>, <i>Acinetobacter</i>, <i>Pseudomonas</i>, and <i>Bifidobacterium</i>. Bacterial diversity was lower in culture-positive samples for <i>Streptococcus pneumoniae</i>, and <i>Haemophilus influenzae</i>, compared to cultured-negative samples. URI frequencies were positively associated with increasing trend in otopathogen colonization. AOM frequencies were associated with decreasing trend in <i>Micrococcus</i> colonization. During URI and AOM, there were increases in abundance of otopathogen genera and decreases in <i>Pseudomonas</i>, <i>Myroides</i>, <i>Yersinia</i>, <i>and Sphingomonas</i>. Otopathogen abundance was increased during symptomatic viral infection, but not during asymptomatic infection. The risk for AOM complicating URI was reduced by increased abundance of <i>Staphylococcus and Sphingobium</i>.</p><p>Conclusion</p><p>Otopathogen genera played the key roles in URI and AOM occurrences. <i>Staphylococcus</i> counteracts otopathogens thus <i>Staphylococcal</i> colonization may be beneficial, rather than harmful. While <i>Sphingobium</i> may play a role in preventing AOM complicating URI, the commonly used probiotic <i>Bifidobacterium</i> did not play a significant role during URI or AOM. The role of less common commensals in counteracting the deleterious effects of otopathogens requires further studies.</p></div

Relative abundance of microbiota in samples from subjects without and with AOM in the first year.

<p>Relative abundance of microbiota in samples from subjects without and with AOM in the first year.</p

Characteristics of subjects and number of specimens.

<p>Characteristics of subjects and number of specimens.</p