miR-29a regulates vascular neointimal hyperplasia by targeting YY1
Authors: Xin-Hua Zhang
Cui-Ying Zheng
Yong Hui Li
Wei-Na Yin
Chan-Juan Nie
Shu-Feng Liu
Yu-Xin Jin
Chao Liu
Bin Zheng
Ming-Hui Ren
Jin-Kun Wen
Yu Zhang
Wen Jiang
Journal: Cell Proliferation (2017)
Discipline: MOLECULAR BIOLOGY & GENETICS
Keywords: vascular neointima; miR-29a; smooth muscle cells; hyperplasia; proliferation
Affiliations: Department of Biochemistry and Molecular Biology, Hebei Medical University, Shijiazhuang, China., Hebei Center for Disease Control and Prevention, Shijiazhuang, China., Laboratory Animal Center of Hebei Medical University, Shijiazhuang, China., The Third Hospital of Hebei Medical University, Shijiazhuang, China., Handan First Hospital, Handan, China.

Abstract:  Objectives: The formation of vascular neointima is mainly related to impairment of the vascular endothelial barrier and abnormal proliferation and migration of smooth muscle cells. The objective of this study was to investigate whether miR-29a exerts any promoting effect on the vascular neointimal hyperplasia and if so, its mechanism. Materials and methods: RT-qPCR was performed to determine expression of miR-29a in vascular smooth muscle cells (VSMC) and vascular neointimal hyperplasia. To further understand its role, we restored its expression in VSMCs by transfection with miR-29a mimics or inhibitors. Effects of miR-29a on cell proliferation were also determined. Results: In this study, we used two kinds of model to observe the role of miR-29a in neointimal hyperplasia induced by carotid ligation or balloon injury. The major findings were that: (i) miR-29a overexpression promoted neointimal hyperplasia induced by carotid ligation; (ii) miR-29a increased proliferation of VSMCs, one aspect of which was by targeting expression of Ying and yang 1 protein (YY1), a negative regulator of Cyclin D1. A further aspect, was by increasing expression of Kr√ºppel-like factor 5, a positive regulator of Cyclin D1, thereby allowing formation a synergistic effect. (iii) Tongxinluo (TXL), a traditional Chinese medicine reduced neointimal formation in ligated vessels by inhibiting VSMC proliferation and migration. Conclusions: These findings provide a new molecular mechanism of TXL in decreasing neointima hyperplasia. 

Retracted: No

PubPeer comments:
 In Stewart et al. (2018), tables 2-4 include regression results. The standard errors are identical to the third decimal place across models. The web links are to pictures of the tables. Stable standard errors are in yellow. Some stability in standard errors is normal, especially with a sample of this size, but this level of stability is unusual given the observed changes both in the regression coefficients and in the amount of explained variance. Combined there are 548 regression coefficients and standard errors in these three tables, but just one ends with a zero in the third decimal place. This is also unusual because the distribution of third-decimal-place numbers (with rounding) should be close to uniform. https: imgur.com pcdB5XF https: imgur.com nEft7lQ https: imgur.com iDDs2as For binary variables, it is possible to calculate the standard deviation knowing the sample size (N) and variable mean (P), the proportion of respondents coded 1. The formula is: SD = sqrt {[P*(1 u2013 P)*N] N u2013 1}. In Stewart et al. (2018), the standard deviations for four of the binary variables are very inaccurate, too inaccurate to be due to rounding. The web link is to the Stewart et al. (2018) table with the errors highlighted. Given the listed means and sample size, the correct standard deviations are: married (.499), education (.497), political conservative (.495), owns home (.463). https: imgur.com rOzyqDF Stewart et al. (2018) use a sample of 1,144 Southern whites in 90 counties. Stewart et al. (2019) use a sample of 2,408 Whites in 168 counties. Despite the large difference in sample size, these two samples have identical means and or standard deviations on ten variables. It is unlikely that this is the result of the authors accidentally including the wrong table in one of the papers, because the samples do differ on several variables (e.g., family income, political conservatism). The following web link is to the tables. Matching descriptive statistics are in yellow. Sample size differences are in red. https: imgur.com pW9YvrX Stewart et al. (2018, p. 471) provide the only exact p-values in the study for supplementary analyses: u201cWe thus estimated models consistent with those in tables 2 and 3 but focused instead on the Black respondents (n = 200). Black lynchings did not yield a statistically significant effect on punitive-Black sentiment (b = .065, standard error [SE] =.063, p = .35) or the Black-White punitive sentiment ratio (b = .059, SE = .058, p = .39). But the provided p-values do not match the provided coefficients and standard errors. The values should instead be: p = .30 and p = .31. https: imgur.com X65TQkQ The total sample includes 2,736 Americans, from a 2013 survey that used u201ca two-stage modified Mitofskyu2013Waksberg sampling designu201d to randomly sample u201chouseholds with landlines or cell phonesu201d (Stewart et al., 2018, p. 460). Oddly, the authors never identify the survey company that conducted this survey. Mitofskyu2013Waksberg sampling is rarely used for cell phones. There is substantial error in matching wireless numbers to counties. According to a Pew Report by Christian et al. (2009): u201cThe geographic information derived from cell phone numbers is subject to a great deal of error u2026 the sample and zip code-derived county do not match for nearly four-in-ten cell respondents (39%).u201d http: www.pewresearch.org 2009 07 09 accurately-locating-where-wireless-respondents-live-requires-more-than-a-phone-number The article never mentions a funding agency or grant number, which is surprising, because a nationally representative, dual-frame, telephone survey of 2,736 Americans would cost well over $100,000. https: www.surveypractice.org article 3168-the-changing-costs-of-random-digital-dial-cell-phone-and-landline-interviewing The differences between the 2013 sample characteristics and the US population are also difficult to reconcile with the use of random sampling for a sample this large. The total sample includes 2,736 Americans, of whom 2,408 are non-Latino White respondents. This means 88% of respondents are non-Latino Whites. According to the US Census, however, only 60.7% of Americans are non-Latino Whites. A random sample of this size should not be 27 percentage points off the population value. Stewart et al. (2018, p. 461) write that the u201csouthern sample [of Whites] in our analysis consists of 1,441 respondents who resided in 90 counties across these 11 states.u201d This means that 60% of all whites in the sample (1,441 2,408) lived in 11 southern states. This is odd, because in their Social Problems article, Stewart et al. (2019) give a different percentage: https: imgur.com tP8EWhk It is also improbable because, according to the US Census, only 37.5% of all Americans lived in the South in 2013. However, the percentage of White Americans living in the South was even lower than that, because the South is the second most racially diverse region. In a large nationally representative survey, one would expect about 30-35% of White respondents to live in the South, not 55-60%. A large random sample should not be off by over 20 percentage points. https: www.census.gov popclock print.php?component=growth image= www.census.gov popclock share images growth_1530403200.png https: statisticalatlas.com United-States Race-and-Ethnicity#figure region ,