Protein arginine methyltransferases (PRMTs) are a family of enzymes that modify proteins by methylating the guanidino nitrogen atoms of arginine residues to regulate cellular processes such as chromatin remodeling, pre-mRNA splicing, and signal transduction. PRMT7 is the single type III PRMT solely capable of arginine monomethylation. To date, other than histone proteins, there are very few identified substrates of PRMT7. We therefore performed quantitative mass spectrometry experiments to identify PRMT7’s interactome and potential substrates to better characterize the enzyme’s biological function(s) in cells. These experiments revealed that PRMT7 interacts with and can methylate eukaryotic translation initiation factor 2 alpha (eIF2α), in vitro and in breast cancer cells. Furthermore, we uncovered a potential regulatory interplay between eIF2α arginine methylation by PRMT7 and stress-induced phosphorylation status of eIF2α at serine 51. Finally, we demonstrated that PRMT7 is required for eIF2α-dependent stress granule formation in the face of various cellular stresses. Altogether, our findings implicate PRMT7 as a novel mediator of eIF2α-dependent cellular stress response pathways.
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Background P-hacking, the tendency to run selective analyses until they become significant, is prevalent in many scientific disciplines. Purpose This study aims to assess if p-hacking exists in imaging research. Methods Protocol, data, and code available here https://osf.io/xz9ku/?view_only=a9f7c2d841684cb7a3616f567db273fa . We searched imaging journals Ovid MEDLINE from 1972 to 2021. Text mining using Python script was used to collect metadata: journal, publication year, title, abstract, and P-values from abstracts. One P-value was randomly sampled per abstract. We assessed for evidence of p-hacking using a p-curve, by evaluating for a concentration of P-values just below .05. We conducted a one-tailed binomial test (α = .05 level of significance) to assess whether there were more P-values falling in the upper range (e.g., .045 < P < .05) than in the lower range (e.g., .04 < P < .045). To assess variation in results introduced by our random sampling of a single P-value per abstract, we repeated the random sampling process 1000 times and pooled results across the samples. Analysis was done (divided into 10-year periods) to determine if p-hacking practices evolved over time. Results Our search of 136 journals identified 967,981 abstracts. Text mining identified 293,687 P-values, and a total of 4105 randomly sampled P-values were included in the p-hacking analysis. The number of journals and abstracts that were included in the analysis as a fraction and percentage of the total number was, respectively, 108/136 (80%) and 4105/967,981 (.4%). P-values did not concentrate just under .05; in fact, there were more P-values falling in the lower range (e.g., .04 < P < .045) than falling just below .05 (e.g., .045 < P < .05), indicating lack of evidence for p-hacking. Time trend analysis did not identify p-hacking in any of the five 10-year periods. Conclusion We did not identify evidence of p-hacking in abstracts published in over 100 imaging journals since 1972. These analyses cannot detect all forms of p-hacking, and other forms of bias may exist in imaging research such as publication bias and selective outcome reporting.
Background Biparametric (bp)‐MRI and multiparametric (mp)‐MRI may improve the diagnostic accuracy of renal mass histology. Purpose To evaluate the available evidence on the diagnostic accuracy of bp‐MRI and mp‐MRI for solid renal masses in differentiating malignant from benign, aggressive from indolent, and clear cell renal cell carcinoma (ccRCC) from other histology. Study Type Systematic review. Population MEDLINE, EMBASE, and CENTRAL up to January 11, 2022 were searched. Field Strength/Sequence 1.5 or 3 Tesla. Assessment Eligible studies evaluated the accuracy of MRI (with at least two sequences: T2, T1, dynamic contrast and diffusion‐weighted imaging) for diagnosis of solid renal masses in adult patients, using histology as reference standard. Risk of bias and applicability were assessed using QUADAS‐2. Statistical Tests Meta‐analysis using a bivariate logitnormal random effects model. Results We included 10 studies (1239 masses from approximately 1200 patients). The risk of bias was high in three studies, unclear in five studies and low in two studies. The diagnostic accuracy of malignant (vs. benign) masses was assessed in five studies (64% [179/281] malignant). The summary estimate of sensitivity was 95% (95% confidence interval [CI]: 77%–99%), and specificity was 63% (95% CI: 46%–77%). No study assessed aggressive (vs. indolent) masses. The diagnostic accuracy of ccRCC (vs. other subtypes) was evaluated in six studies (47% [455/971] ccRCC): the summary estimate of sensitivity was 85% (95% CI: 77%–90%) and specificity was 77% (95% CI: 73%–81%). Data Conclusion Our study reveals deficits in the available evidence on MRI for diagnosis of renal mass histology. The number of studies was limited, at unclear/high risk of bias, with heterogeneous definitions of solid masses, imaging techniques, diagnostic criteria, and outcome measures. Evidence Level 3 Technical Efficacy Stage 2
The ongoing coronavirus disease 2019 (COVID-19) pandemic continues to present diagnostic challenges. The use of thoracic radiography has been studied as a method to improve the diagnostic accuracy of COVID-19. The ‘Living’ Cochrane Systematic Review on the diagnostic accuracy of imaging tests for COVID-19 is continuously updated as new information becomes available for study. In the most recent version, published in March 2021, a meta-analysis was done to determine the pooled sensitivity and specificity of chest X-ray (CXR) and lung ultrasound (LUS) for the diagnosis of COVID-19. CXR gave a sensitivity of 80.6% (95%CI: 69.1-88.6) and a specificity of 71.5% (95%CI: 59.8-80.8). LUS gave a sensitivity rate of 86.4% (95%CI: 72.7-93.9) and specificity of 54.6% (95%CI: 35.3-72.6). These results differed from the findings reported in the recent article in this journal where they cited the previous versions of the study in which a meta-analysis for CXR and LUS could not be performed. Additionally, the article states that COVID-19 could not be distinguished, using chest computed tomography (CT), from other respiratory diseases. However, the latest review version identifies chest CT as having a specificity of 80.0% (95%CI: 74.9-84.3), which is much higher than the previous version which indicated a specificity of 61.1% (95%CI: 42.3-77.1). Therefore, CXR, chest CT and LUS have the potential to be used in conjunction with other methods in the diagnosis of COVID-19.
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