Plasma microRNAs as potential biomarkers for non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) is the leading cause of cancer-related death. Developing minimally invasive techniques that can diagnose NSCLC, particularly at an early stage, may improve its outcome. Using microarray platforms, we previously identified 12 microRNAs (miRNAs) the aberrant expressions of which in primary lung tumors are associated with early-stage NSCLC. Here, we extend our previous research by investigating whether the miRNAs could be used as potential plasma biomarkers for NSCLC. We initially validated expressions of the miRNAs in paired lung tumor tissues and plasma specimens from 28 stage I NSCLC patients by real-time quantitative reverse transcription PCR, and then evaluated diagnostic value of the plasma miRNAs in a cohort of 58 NSCLC patients and 29 healthy individuals. The altered miRNA expressions were reproducibly confirmed in the tumor tissues. The miRNAs were stably present and reliably measurable in plasma. Of the 12 miRNAs, five displayed significant concordance of the expression levels in plasma and the corresponding tumor tissues (all r>0.850, all P<0.05). A logistic regression model with the best prediction was defined on the basis of the four genes (miRNA-21, -126, -210, and 486-5p), yielding 86.22% sensitivity and 96.55% specificity in distinguishing NSCLC patients from the healthy controls. Furthermore, the panel of miRNAs produced 73.33% sensitivity and 96.55% specificity in identifying stage I NSCLC patients. In addition, the genes have higher sensitivity (91.67%) in diagnosis of lung adenocarcinomas compared with squamous cell carcinomas (82.35%) (P<0.05). Altered expressions of the miRNAs in plasma would provide potential blood-based biomarkers for NSCLC

NMR hyperpolarization techniques of gases

Nuclear spin polarization can be significantly increased through the process of hyperpolarization, leading to an increase in the sensitivity of nuclear magnetic resonance (NMR) experiments by 4–8 orders of magnitude. Hyperpolarized gases, unlike liquids and solids, can often be readily separated and purified from the compounds used to mediate the hyperpolarization processes. These pure hyperpolarized gases enabled many novel MRI applications including the visualization of void spaces, imaging of lung function, and remote detection. Additionally, hyperpolarized gases can be dissolved in liquids and can be used as sensitive molecular probes and reporters. This Minireview covers the fundamentals of the preparation of hyperpolarized gases and focuses on selected applications of interest to biomedicine and materials science

A Comprehensive tRNA Deletion Library Unravels the Genetic Architecture of the tRNA Pool

<div><p>Deciphering the architecture of the tRNA pool is a prime challenge in translation research, as tRNAs govern the efficiency and accuracy of the process. Towards this challenge, we created a systematic tRNA deletion library in <i>Saccharomyces cerevisiae</i>, aimed at dissecting the specific contribution of each tRNA gene to the tRNA pool and to the cell's fitness. By harnessing this resource, we observed that the majority of tRNA deletions show no appreciable phenotype in rich medium, yet under more challenging conditions, additional phenotypes were observed. Robustness to tRNA gene deletion was often facilitated through extensive backup compensation within and between tRNA families. Interestingly, we found that within tRNA families, genes carrying identical anti-codons can contribute differently to the cellular fitness, suggesting the importance of the genomic surrounding to tRNA expression. Characterization of the transcriptome response to deletions of tRNA genes exposed two disparate patterns: in single-copy families, deletions elicited a stress response; in deletions of genes from multi-copy families, expression of the translation machinery increased. Our results uncover the complex architecture of the tRNA pool and pave the way towards complete understanding of their role in cell physiology.</p></div

Differential contribution of identical tRNA gene copies.

<p>(A–B) Relative growth yield values of the tRNA deletion library strains in rich medium (A) and low glucose (B), sorted by anti-codon and amino-acid identity along the x-axis. Each dot along the vertical lines denotes the value (data are represented as mean of 3 biological repetitions +/− SEM) of a deletion strain of different tRNA gene of the respective family. The horizontal lines mark two standard deviations around the mean of the wild-type. Dots above or below these lines are considered non-normal phenotypes (see also Supplemental <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004084#pgen.1004084.s007" target="_blank">figure S7</a>). (C) Relative growth yield values (data are presented as mean of 3 biological repetitions +/− SEM) of various double deletion combinations consisting of: five <i>tR(UCU)</i> family members, <i>tR(CCU)</i> and <i>trm9</i> deletion strains as indicated on the x-axis, along with the five members of the <i>tR(UCU)</i> family each denoted by a different shape and color in the legend. (D) Relative growth yield of the five <i>tR(UCU)</i> members across different growth conditions, indicated on the x-axis. (E) Enrichment of conserved elements in tRNA genes divided according to phenotype observed in rich media for each growth parameter. Each column in the matrix denotes a conserved element as defined by <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004084#pgen.1004084-Giuliodori1" target="_blank">[42]</a>. Color bar indicates the −log₁₀ of the hypergeometric p-value. (F) log10 E-value found by the MEME software for the most significant motif in a 9 bp window starting from the position indicated by the x-axis. The LOGOs of the two significant motifs are displayed below, next to a number indicating its position. Position 0 is the first position of the mature tRNA.</p

KEGG pathways differentiating between tRNA deletion sets.

<p>KEGG pathways <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004084#pgen.1004084-Kanehisa1" target="_blank">[49]</a> for which changes in genes expression are significantly different between the two groups of tRNA deletion strains: MC (multi-copy) group (<i>ΔtH(GUG)G1</i> and <i>ΔtR(UCU)M2</i>) vs. SC (single-copy) group (<i>ΔtL(GAG)G</i>, <i>ΔtR(CCU)J</i>, <i>ΔtiM(CAU)C</i>) calculated with GSEA <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004084#pgen.1004084-Subramanian1" target="_blank">[51]</a>, <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004084#pgen.1004084-Mootha1" target="_blank">[52]</a>. In the first column are pathways, which are higher in SC vs. MC and vice versa in the second column. The values are corrected for multiple hypothesis and the FDR q-values are indicated next to each pathway.</p