From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

<p>Abstract</p> <p>Background</p> <p>Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification.</p> <p>Results</p> <p>In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model.</p> <p>Conclusions</p> <p>FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context.</p

Anti-Cancer Effect of HIV-1 Viral Protein R on Doxorubicin Resistant Neuroblastoma

Several unique biological features of HIV-1 Vpr make it a potentially powerful agent for anti-cancer therapy. First, Vpr inhibits cell proliferation by induction of cell cycle G2 arrest. Second, it induces apoptosis through multiple mechanisms, which could be significant as it may be able to overcome apoptotic resistance exhibited by many cancerous cells, and, finally, Vpr selectively kills fast growing cells in a p53-independent manner. To demonstrate the potential utility of Vpr as an anti-cancer agent, we carried out proof-of-concept studies in vitro and in vivo. Results of our preliminary studies demonstrated that Vpr induces cell cycle G2 arrest and apoptosis in a variety of cancer types. Moreover, the same Vpr effects could also be detected in some cancer cells that are resistant to anti-cancer drugs such as doxorubicin (DOX). To further illustrate the potential value of Vpr in tumor growth inhibition, we adopted a DOX-resistant neuroblastoma model by injecting SK-N-SH cells into C57BL/6N and C57BL/6J-scid/scid mice. We hypothesized that Vpr is able to block cell proliferation and induce apoptosis regardless of the drug resistance status of the tumors. Indeed, production of Vpr via adenoviral delivery to neuroblastoma cells caused G2 arrest and apoptosis in both drug naïve and DOX-resistant cells. In addition, pre-infection or intratumoral injection of vpr-expressing adenoviral particles into neuroblastoma tumors in SCID mice markedly inhibited tumor growth. Therefore, Vpr could possibly be used as a supplemental viral therapeutic agent for selective inhibition of tumor growth in anti-cancer therapy especially when other therapies stop working

Rad3ATR Decorates Critical Chromosomal Domains with γH2A to Protect Genome Integrity during S-Phase in Fission Yeast

Schizosaccharomyces pombe Rad3 checkpoint kinase and its human ortholog ATR are essential for maintaining genome integrity in cells treated with genotoxins that damage DNA or arrest replication forks. Rad3 and ATR also function during unperturbed growth, although the events triggering their activation and their critical functions are largely unknown. Here, we use ChIP-on-chip analysis to map genomic loci decorated by phosphorylated histone H2A (γH2A), a Rad3 substrate that establishes a chromatin-based recruitment platform for Crb2 and Brc1 DNA repair/checkpoint proteins. Unexpectedly, γH2A marks a diverse array of genomic features during S-phase, including natural replication fork barriers and a fork breakage site, retrotransposons, heterochromatin in the centromeres and telomeres, and ribosomal RNA (rDNA) repeats. γH2A formation at the centromeres and telomeres is associated with heterochromatin establishment by Clr4 histone methyltransferase. We show that γH2A domains recruit Brc1, a factor involved in repair of damaged replication forks. Brc1 C-terminal BRCT domain binding to γH2A is crucial in the absence of Rqh1Sgs1, a RecQ DNA helicase required for rDNA maintenance whose human homologs are mutated in patients with Werner, Bloom, and Rothmund–Thomson syndromes that are characterized by cancer-predisposition or accelerated aging. We conclude that Rad3 phosphorylates histone H2A to mobilize Brc1 to critical genomic domains during S-phase, and this pathway functions in parallel with Rqh1 DNA helicase in maintaining genome integrity

Vpr induces cell cycle G2 arrest in various cancer cell types.

<p>All cancer cell lines (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011466#pone-0011466-t001" target="_blank">Table 1</a> for details; only 3 cell lines are shown here as examples) were grown as described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011466#s4" target="_blank">Materials and Methods</a>. Cells were transduced with Adv or Adv-Vpr with MOI of 1.0. The cells were harvested 48 hrs post-infection (<i>p.i.</i>), Cells were then prepared as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011466#s4" target="_blank">Materials and Methods</a>. Cellular DNA content was analyzed by FACScan flow cytometry (Becton Dickinson) as we previously described <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011466#pone.0011466-Li1" target="_blank">[4]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011466#pone.0011466-Liang1" target="_blank">[19]</a>. The cell cycle profiles were modeled using ModFit software (Verity Software House, Inc.).</p

Vpr suppresses tumor regression in a neuroblastoma mouse model.

<p>Suppression of neuroblastoma tumor growth by Vpr is demonstrated here either by pre-transduction of Adv-Vpr (<b>A-B</b>) or post-intratumoral injection (<b>C</b>). For pre-transduction, wild type (WT) or DOX-resistant SK-N-SH were grown in DMEM supplemented with 10% FBS at 37°C with a 95%Air/5%CO₂ atmosphere. Fresh cells were first grown in a 12-well plate for 36 hours and adenoviral transduction was carried out 5 hours before cell inoculation with MOI of 2.5, which was determined empirically. Cells were then treated with Trpsin- EDTA, re-suspended in DMEM and washed with PBS 3 times. Final cells were suspended in DMEM for inoculation. About 2×10⁶ cells in the volume of 100 µl were injected <i>s.c.</i> in the left flank of C57-SCID mice. 3–4 mice were injected for each treatment. These treatment groups include an Adv viral control, Adv-Vpr and Adv-F34IVpr. The F34IVpr mutant was used here as a control since a single amino acid change of amino acid 34 from Phenylalanine (F) to Isoleucine (I) renders Vpr unable to cause apoptosis (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011466#pone-0011466-g003" target="_blank">Figure 3C</a>) but allows for the cell cycle to enter a prolonged G2 phase <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011466#pone.0011466-Benko1" target="_blank">[18]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011466#pone.0011466-Chen1" target="_blank">[27]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011466#pone.0011466-Vodicka1" target="_blank">[28]</a>. The tumor size was measured every 7 days by measuring two perpendicular tumor diameters using calipers. Final tumor measurement was at 26 days post-transduction and mice were then sacrificed for further analysis. For intra-lesional injection of Vpr, the WT and DOX-R SK-N-SH neuroblastoma cells were prepared essentially the same way as described above. 200 µl of the Adv, Adv-Vpr or Adv-F34Ivpr was then injected discretely 3-times into the tumors 2 weeks after cell inoculation with a viral concentration of 1,012/ml. The tumor size was measured every 7 days. Final measurement of tumor size was at 39 days post-injection and mice were then sacrificed for further analysis. Three independent experiments were carried out and results of these experiments with average tumor size with standard deviation (SD) are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011466#pone-0011466-t002" target="_blank">Table 2</a>.</p

Summary of Vpr-induced tumor regression of WT and DOX-resistant neuroblastoma in C57-SCID mice.

<p><b>Note:</b> Tumor sizes were measured at 39 days post-intratumoral injection (Week 0). Levels of statistical significance of the t-test results between the Adv control and the testing groups (Ad-Vpr or Ad-F34IVpr): *, p<0.05; **, p<0.001; Weighted average sums of the t-tests <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011466#pone.0011466-Tan1" target="_blank">[31]</a> for both wild type and Dox-R mice showed statistic differences at the level of p<0.05. Note: na, non-applicable.</p

Vpr induces cell death and apoptosis in DOX-naïve and resistant SK-N-SH cells.

<p><b>A.</b> Vpr induces dose-dependent cell death in drug-naïve and resistant SK-N-SH cells. <b>a.</b> Level of Vpr-induced cell death was examined by infecting DOX-naïve and resistant SK-N-SH cells using increasing MOI of Adv or Adv-Vpr viruses. Cell death was measured by determining the cell membrane integrity and proliferation using Trypan blue straining (left) or cell viability by the MTT assay (right). Cells proliferation and viability were examined 5 days <i>p.i</i>. <b>b.</b> Vpr protein production was confirmed by Western blot analysis. Note that it is very difficult to detect Vpr protein at low MOI. Successful infection of Adv-Vpr was verified by enlarged nuclei of cells as previously reported <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011466#pone.0011466-Bartz1" target="_blank">[43]</a>. <b>B.</b> Vpr induces cell death over time in drug- naïve and resistant SK-N-SH cells. Both drug-naïve and resistant SK-N-SH cells treated the same way as <b>A</b>. Only MOI2.5 was used here. <b>C.</b> Vpr induces apoptosis in drug-naïve and resistant SK-N-SH cells. Caspase-3 cleavage was monitored up to 36 hours. Initials: Csp3, caspase-3; cl-Csp3, cleaved caspase-3; m, Adv-VprF34I; w, Adv-Vpr.</p