Melanoma-Derived DNA Polymerase Theta Variants Exhibit Altered DNA Polymerase Activity.

DNA polymerase θ (Pol θ or POLQ) is primarily involved in repairing double-stranded breaks in DNA through an alternative pathway known as microhomology-mediated end joining (MMEJ) or theta-mediated end joining (TMEJ). Unlike other DNA repair polymerases, Pol θ is thought to be highly error-prone yet critical for cell survival. We have identified several POLQ gene variants from human melanoma tumors that experience altered DNA polymerase activity, including a propensity for incorrect nucleotide selection and reduced polymerization rates compared to WT Pol θ. Variants are 30-fold less efficient at incorporating a nucleotide during repair and up to 70-fold less accurate at selecting the correct nucleotide opposite a templating base. This suggests that aberrant Pol θ has reduced DNA repair capabilities and may also contribute to increased mutagenesis. Moreover, the variants were identified in established tumors, suggesting that cancer cells may use mutated polymerases to promote metastasis and drug resistance.

Melanoma-derived DNA polymerase theta variants exhibit altered DNA polymerase activity.

DNA Polymerase θ (Pol θ or POLQ) is primarily involved in repairing double-stranded breaks in DNA through the alternative pathway known as microhomology-mediated end joining (MMEJ) or theta-mediated end joining (TMEJ). Unlike other DNA repair polymerases, Pol θ is thought to be highly error prone, yet critical for cell survival. We have identified several mutations in the POLQ gene from human melanoma tumors. Through biochemical analysis, we have demonstrated that all three cancer-associated variants experienced altered DNA polymerase activity including a propensity for incorrect nucleotide selection and reduced polymerization rates compared to WT Pol θ. Moreover, the variants are 30 fold less efficient at incorporating a nucleotide during repair and up to 70 fold less accurate at selecting the correct nucleotide opposite a templating base. Taken together, this suggests that aberrant Pol θ has reduced DNA repair capabilities and may also contribute to increased mutagenesis. While this may be beneficial to normal cell survival, the variants were identified in established tumors suggesting that cancer cells may use this promiscuous polymerase to its advantage to promote metastasis and drug resistance.

The effect of mobile camera selection on the capacity to predict water turbidity.

Recently, cameras of mobile have phones emerged as an alternative for quantifying water turbidity. Most of these studies lack a strategy to determine the water turbidity for new samples, focusing mainly on one particular device. Nevertheless, widespread use of these approaches requires a predictive capacity on out-of-the-sample images acquired in devices of different capabilities. We studied the influence of mobile device camera sensors on the predictive performance of water turbidity for non-previously observed turbid images. For this, a reference database with turbid images acquired for different mobile devices was constructed. A machine learning method based on image quality measures and linear classifiers (least squares and LASSO) was proposed to perform predictions on each mobile device. Relative accuracy and precision were evaluated. Results suggest that these approaches may provide accurate predictions reaching most than 80% of relative accuracy with high test-retest reliability (> 0.99). Nevertheless, our results also indicate that the predictive performance levels dropped in low capacity quality sensors. Therefore, despite the high performance that can be reached using these approaches, widespread use on multiple mobile devices may require further development of low-quality sensors and a better understanding of their operative ranges.

Correction to: Contour analysis for interpretable leaf shape category discovery.

[This corrects the article DOI: 10.1186/s13007-019-0497-6.].

Contour analysis for interpretable leaf shape category discovery.

BACKGROUND: The categorical description of leaf shapes is of paramount importance in ecology, taxonomy and paleobotanical studies. Classification systems proposed by domain experts support these descriptions. Despite the importance of these visual descriptive systems, classifications based on this expert's knowledge may be ambiguous or limited when representing shapes in unknown scenarios, as expected for biological exploratory domains. This work proposes a novel strategy to automatically discover the shape categories in a set of unlabeled leaves by only using the leaf-shape information. In particular, we overcome the task of discovering shape categories from different plant species for three different biological settings. RESULTS: The proposed method may successfully infer the unknown underlying shape categories with an F-score greater than 92%. CONCLUSIONS: The approach also provided high levels of visual interpretability, an essential requirement in the description of biological objects. This method may support morphological analysis of biological objects in exploratory domains.