Application of an active attachment model as a high-throughput demineralization biofilm model

Objectives: To investigate the potential of an active attachment biofilm model as a highthroughput demineralization biofilm model for the evaluation of caries-preventive agents. Methods: Streptococcus mutans UA159 biofilms were grown on bovine dentine discs in a highthroughput active attachment model. Biofilms were first formed in a medium with high buffer capacity for 24 h and then subjected to various photodynamic therapies (PACT) using the combination of Light Emitting Diodes (LEDs, Biotable (R)) and Photogem (R). Viability of the biofilms was evaluated by plate counts. To investigate treatment effects on dentine lesion formation, the treated biofilms were grown in a medium with low buffer capacity for an additional 24 h. Integrated mineral loss (IML) and lesion depth (LD) were assessed by transversal microradiography. Calcium release in the biofilm medium was measured by atomic absorption spectroscopy. Results: Compared to the water treated control group, significant reduction in viability of S. mutans biofilms was observed when the combination of LEDs and Photogem (R) was applied. LEDs or Photogem (R) only did not result in biofilm viability changes. Similar outcomes were also found for dentine lesion formation. Significant lower IML and LD values were only found in the group subjected to the combined treatment of LEDs and Photogem (R). There was a good correlation between the calcium release data and the IML or LD values. Conclusions: The high-throughput active attachment biofilm model is applicable for evaluating novel caries-preventive agents on both biofilm and demineralization inhibition. PACT had a killing effect on 24 h S. mutans biofilms and could inhibit the demineralization process. (C) 2011 Elsevier Ltd. All rights reserved.

High-throughput sequencing of black pepper root transcriptome

Background: Black pepper (Piper nigrum L.) is one of the most popular spices in the world. It is used in cooking and the preservation of food and even has medicinal properties. Losses in production from disease are a major limitation in the culture of this crop. The major diseases are root rot and foot rot, which are results of root infection by Fusarium solani and Phytophtora capsici, respectively. Understanding the molecular interaction between the pathogens and the host's root region is important for obtaining resistant cultivars by biotechnological breeding. Genetic and molecular data for this species, though, are limited. In this paper, RNA-Seq technology has been employed, for the first time, to describe the root transcriptome of black pepper. Results: The root transcriptome of black pepper was sequenced by the NGS SOLiD platform and assembled using the multiple-k method. Blast2Go and orthoMCL methods were used to annotate 10338 unigenes. The 4472 predicted proteins showed about 52% homology with the Arabidopsis proteome. Two root proteomes identified 615 proteins, which seem to define the plant's root pattern. Simple-sequence repeats were identified that may be useful in studies of genetic diversity and may have applications in biotechnology and ecology. Conclusions: This dataset of 10338 unigenes is crucially important for the biotechnological breeding of black pepper and the ecogenomics of the Magnoliids, a major group of basal angiosperms.

Simcluster: clustering enumeration gene expression data on the simplex space

Abstract Background Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space. Results Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at: http://xerad.systemsbiology.net/simcluster. Conclusion Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus applicable in a number of contexts, including enumeration-based gene expression data.

High-throughput sequencing of small RNA transcriptomes reveals critical biological features targeted by microRNAs in cell models used for squamous cell cancer research

Abstract Background The implication of post-transcriptional regulation by microRNAs in molecular mechanisms underlying cancer disease is well documented. However, their interference at the cellular level is not fully explored. Functional in vitro studies are fundamental for the comprehension of their role; nevertheless results are highly dependable on the adopted cellular model. Next generation small RNA transcriptomic sequencing data of a tumor cell line and keratinocytes derived from primary culture was generated in order to characterize the microRNA content of these systems, thus helping in their understanding. Both constitute cell models for functional studies of microRNAs in head and neck squamous cell carcinoma (HNSCC), a smoking-related cancer. Known microRNAs were quantified and analyzed in the context of gene regulation. New microRNAs were investigated using similarity and structural search, ab initio classification, and prediction of the location of mature microRNAs within would-be precursor sequences. Results were compared with small RNA transcriptomic sequences from HNSCC samples in order to access the applicability of these cell models for cancer phenotype comprehension and for novel molecule discovery. Results Ten miRNAs represented over 70% of the mature molecules present in each of the cell types. The most expressed molecules were miR-21, miR-24 and miR-205, Accordingly; miR-21 and miR-205 have been previously shown to play a role in epithelial cell biology. Although miR-21 has been implicated in cancer development, and evaluated as a biomarker in HNSCC progression, no significant expression differences were seen between cell types. We demonstrate that differentially expressed mature miRNAs target cell differentiation and apoptosis related biological processes, indicating that they might represent, with acceptable accuracy, the genetic context from which they derive. Most miRNAs identified in the cancer cell line and in keratinocytes were present in tumor samples and cancer-free samples, respectively, with miR-21, miR-24 and miR-205 still among the most prevalent molecules at all instances. Thirteen miRNA-like structures, containing reads identified by the deep sequencing, were predicted from putative miRNA precursor sequences. Strong evidences suggest that one of them could be a new miRNA. This molecule was mostly expressed in the tumor cell line and HNSCC samples indicating a possible biological function in cancer. Conclusions Critical biological features of cells must be fully understood before they can be chosen as models for functional studies. Expression levels of miRNAs relate to cell type and tissue context. This study provides insights on miRNA content of two cell models used for cancer research. Pathways commonly deregulated in HNSCC might be targeted by most expressed and also by differentially expressed miRNAs. Results indicate that the use of cell models for cancer research demands careful assessment of underlying molecular characteristics for proper data interpretation. Additionally, one new miRNA-like molecule with a potential role in cancer was identified in the cell lines and clinical samples.