24 research outputs found
Suppression of nucleotidyl transferases affects overall miRNA abundance.
<p>We profiled miRNA expression with the NanoString nCounter platform (left) and miRNA qRT-PCR assays (right) following suppression of a panel of nucleotidyl transferases. Suppression of TUT1, shown in bold, led to the most substantial decreases in miRNA expression compared to cells treated with negative control siRNAs (t-test adjusted p-value <1×10<sup>−10</sup> for both NanoString and qRT-PCR platforms). The table depicts the mean and median relative expression values (RQ) of miRNAs robustly detected across all samples. This included 65 miRNAs from NanoString profiling and 217 miRNAs from qRT-PCR arrays.</p
Association between changes in miRNA 3′ nucleotide additions and changes in miRNA abundance.
<p>MicroRNA 3′ additions and total abundance levels were assessed with NanoString profiling following the suppression of nucleotidyl transferases in HCT-116 cells. (A) Bars depict miRNAs with significant changes in additions compared to negative control cells with a false discovery rate <10%. The bars display the fold change in a given miRNA isomiR, with the color of the bar indicating the 3′ addition affected. The enzyme names above the bars indicate the suppressed nucleotidyl transferase. Data for miRNAs showing decreased addition are from our previous report <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069630#pone.0069630-Wyman1" target="_blank">[25]</a>. (<b>B</b>) Boxplots comparing the abundance changes of miRNAs showing increases, decreases, or no change in 3′ additions. MicroRNAs with significant increases in 3′ additions feature a statistically significant decrease in abundance compared to miRNAs with unchanged or decreased additions (t-test, p-value <0.005). (<b>C</b>) Increased additions are associated with decreased miRNA abundance following suppression of three different nucleotidyl transferase enzymes. Shading of a box indicates a t-test p-value <0.05 when comparing the abundance in miRNAs with unchanged versus increased or decreased 3′ addition. (<b>D</b>) MicroRNAs with increased 3′ U additions show significant decreases in abundance compared to miRNAs with unchanged additions (t-test, p-value <2×10<sup>−5</sup>).</p
The Human TUT1 Nucleotidyl Transferase as a Global Regulator of microRNA Abundance
<div><p>Post-transcriptional modifications of miRNAs with 3′ non-templated nucleotide additions (NTA) are a common phenomenon, and for a handful of miRNAs the additions have been demonstrated to modulate miRNA stability. However, it is unknown for the vast majority of miRNAs whether nucleotide additions are associated with changes in miRNA expression levels. We previously showed that miRNA 3′ additions are regulated by multiple nucleotidyl transferase enzymes. Here we examine the changes in abundance of miRNAs that exhibit altered 3′ NTA following the suppression of a panel of nucleotidyl transferases in cancer cell lines. Among the miRNAs examined, those with increased 3′ additions showed a significant decrease in abundance. More specifically, miRNAs that gained a 3′ uridine were associated with the greatest decrease in expression, consistent with a model in which 3′ uridylation influences miRNA stability. We also observed that suppression of one nucleotidyl transferase, TUT1, resulted in a global decrease in miRNA levels of approximately 40% as measured by qRT-PCR-based miRNA profiling. The mechanism of this global miRNA suppression appears to be indirect, as it occurred irrespective of changes in 3′ nucleotide addition. Also, expression of miRNA primary transcripts did not decrease following TUT1 knockdown, indicating that the mechanism is post-transcriptional. In conclusion, our results suggest that TUT1 affects miRNAs through both a direct effect on 3′ nucleotide additions to specific miRNAs and a separate, indirect effect on miRNA abundance more globally.</p></div
TUT1 suppression yields broad decreases in miRNA expression.
<p>We performed miRNA profiling to determine the effect of suppressing nucleotidyl transferase enzymes in HCT-116 cells on miRNA abundance. (<b>A</b>) We compared the abundance of miRNAs detectable with at least 50 counts on the NanoString platform in the enzyme suppressed vs. negative control siRNA-transfected cells. Boxplots depict miRNA expression relative to negative control cells, which are depicted by the horizontal line at 1. Three enzymes–TUT1, PAPD4, and MTPAP–showed a significant, broad decrease in miRNA abundance (t-test, p-value <1×10<sup>−10</sup>). (<b>B</b>) Boxplots depict the average miRNA expression values from qRT-PCR profiling of over 300 miRNAs in the enzyme-suppressed and negative control cells. In this independent sample set, we found that suppression of TUT1 and PAPD4 lead to highly significant decreases in the abundance of the vast majority of miRNAs analyzed (t-test p-value <1×10<sup>−30</sup> for both enzymes). (<b>C</b>) Suppression of TUT1 in A549 lung carcinoma cells yields decreased miRNA abundance. qRT-PCR arrays were used to compare the global miRNA expression levels in A549 cells transfected with siRNAs against TUT1 or a control nucleotidyl transferase, ZCCHC11, versus negative-control treated cells. A549 cells showed a significant reduction in miRNA abundance following TUT1 suppression (t-test, p-value <1×10<sup>−20</sup>), while ZCCHC11 suppression did not affect miRNA levels.</p
TUT1 knockdown with additional, independent siRNAs confirms the role of TUT1 in maintaining miRNA expression levels.
<p>(<b>A</b>) Two additional TUT1 siRNAs were used to suppress TUT1 in HCT-116 cells. Bar graphs show qRT-PCR assessement of the suppression of TUT1 compared to cells treated with negative control siRNAs. (<b>B</b>) RNAi with both siTUT1-A and siTUT1-B decreased the expression of the TUT1 protein in HCT-116 cells. (<b>C</b>) Exiqon miRNA qRT-PCR arrays were used to compare the global miRNA expression in enzyme suppressed versus negative control cells. Boxplots depict miRNA expression following TUT1 suppression with each of the two new TUT1 siRNAs. Points below the dashed line indicate decreased miRNA abundance with TUT1 suppression compared to the negative control cells. (<b>D</b>) Bar graphs compare the expression of two small nucleolar RNAs, SNORD38B and SNORD49A, in TUT1 supppressed versus control cells. TUT1 suppression did not affect small nucleolar RNA abundance levels, which confirms the specificity of the phenotype to miRNAs.</p
E. coli Chemotaxis
<div><p>
E. coli has been observed to migrate toward areas of higher aspartate concentrations through a series of “runs” and “tumbles” (see
<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0030208#box1" target="_blank">Box 1</a>).
</p>
<p>Autophosph, autophosphorylation.</p></div
Medical Treatments: Reductionism versus Systems Science
<p>Treatment differences stem from divergent problem-solving tactics. Reductionism focuses on components and, in the process, can lose information about time, space, and context. Systems science focuses on the interactions and dynamics and spends less time studying the individual components.</p
Autoantibodies to TP53 discriminate breast cancer sera from control sera.
<p>ROC curves showing the 43 HRN cases (black) and the 28 TN subset (blue) vs. 87 matched healthy controls.</p
Selection of 15 analytes and their sensitivity/specificity as serum markers.
<p>Data from 43 cases and 87 controls. Marker selection criteria: (1) serum marker potential in high-grade serous ovarian cancer and involvement in breast cancer; (2) transcript involved in breast cancer; (3) association with poor breast cancer outcome; (4) protein decreased in breast cancer tissue compared to normal breast.</p><p>Selection of 15 analytes and their sensitivity/specificity as serum markers.</p
Release of protein-biomarkers from uterine fibroids in patients.
<p>The treatment of uterine fibroids with MRg-FUS showed significant (*p≤0.05) increase in release of endothelin-1 (<b>A</b>) CA125 (<b>C</b>) and a not significant increase in CA15-3 p = 0.06 (<b>E</b>), in the post-treatment samples when compared to the pre-treatment plasma samples. The change of pre- versus post-values of each biomarker in individual patients is shown for endothelin-1 (<b>B</b>), CA125 (<b>D</b>) and CA15-3 (<b>F</b>). Box of the plots indicate 25<sup>th</sup> to 75<sup>th</sup> percentile with the median and whiskers are maximum to minimum values.</p