Somatic reprogramming induced by defined transcription factors is a low efficiency process that is enhanced by p53 deficiency 1-5. To date, p21 is the only p53 target shown to contribute to p53 repression of iPSC (induced pluripotent stem cell) generation 1, 3, suggesting additional p53 targets may regulate this process. Here, we demonstrated that mir-34 microRNAs (miRNAs), particularly miR-34a, exhibit p53-dependent induction during reprogramming. mir-34a deficiency in mice significantly increased reprogramming efficiency and kinetics, with miR-34a and p21 cooperatively regulating somatic reprogramming downstream of p53. Unlike p53 deficiency, which enhances reprogramming at the expense of iPSC pluripotency, genetic ablation of mir-34a promoted iPSC generation without compromising self-renewal and differentiation. Suppression of reprogramming by miR-34a was due, at least in part, to repression of pluripotency genes, including Nanog, Sox2 and Mycn (N-Myc). This post-transcriptional gene repression by miR-34a also regulated iPSC differentiation kinetics. miR-34b and c similarly repressed reprogramming; and all three mir-34 miRNAs acted cooperatively in this process. Taken together, our findings identified mir-34 miRNAs as novel p53 targets that play an essential role in restraining somatic reprogramming.

Background. The Kidney Donor Risk Index (KDRI) is a score applicable to deceased kidney donors which reflects relative graft failure risk associated with deceased donor characteristics. The KDRI is widely used in kidney transplant outcomes research. Moreover, an abbreviated version of KDRI is the basis, for allocation purposes, of the “top 20%” designation for deceased donor kidneys. Data upon which the KDRI model was based used kidney transplants performed between 1995 and 2005. Our purpose in this report was to evaluate the need to update the coefficients in the KDRI formula, with the objective of either (a) proposing new coefficients or (b) endorsing continued used of the existing formula. Methods. Using data obtained from the Scientific Registry of Transplant Recipients, we analyzed n = 156069 deceased donor adult kidney transplants occurring from 2000 to 2016. Cox regression was used to model the risk of graft failure. We then tested for differences between the original and updated regression coefficients and compared the performance of the original and updated KDRI formulas with respect to discrimination and predictive accuracy. Results. In testing for equality between the original and updated KDRIs, few coefficients were significantly different. Moreover, the original and updated KDRI yielded very similar risk discrimination and predictive accuracy. Conclusions. Overall, our results indicate that the original KDRI is robust and is not meaningfully improved by an update derived through modeling analogous to that originally employed.

Restricted mean survival time (RMST) is a clinically interpretable and meaningful survival metric that has gained popularity in recent years. Several methods are available for regression modeling of RMST, most based on pseudo-observations or what is essentially an inverse-weighted complete-case analysis. No existing RMST regression method allows for the covariate effects to be expressed as functions over time. This is a considerable limitation, in light of the many hazard regression methods that do accommodate such effects. To address this void in the literature, we propose RMST methods that permit estimating time-varying effects. In particular, we propose an inference framework for directly modeling RMST as a continuous function of L. Large-sample properties are derived. Simulation studies are performed to evaluate the performance of the methods in finite sample sizes. The proposed framework is applied to kidney transplant data obtained from the Scientific Registry of Transplant Recipients (SRTR).

The impact of increasing body mass index (BMI) on development and progression of chronic kidney disease is established. Even implantation kidney biopsies from obese living donors demonstrate subtle histologic changes despite normal function. We hypothesized that kidneys from obese living (LD) and deceased donors (DD) would have inferior long-term allograft outcomes. In a study utilizing US transplant registry, we studied adult kidney transplant recipients from 2000 to 2014. Donors were categorized as BMI <20 (underweight), 20-25 (normal), 25-30 (overweight), 30-35 (mildly obese), and >35 kg/m 2 (very obese). Our outcome of interest was death censored graft failure (DCGF). Cox proportional hazards model were fitted separately for recipients of DD and LD kidneys, and adjusted for donor, recipient, and transplant characteristics, including donor and recipient size mismatch ratio. Among 118 734 DD and 84 377 LD transplants recipients, we observed a significant and graded increase in DCGF risk among the overweight (LD:HR = 1.06, DD:HR = 1.04), mildly obese (LD: HR = 1.16, DD:HR = 1.10), and very obese (LD:HR = 1.22, DD: HR = 1.22) compared to normal BMI (P < 0.05). The graded effect of donor BMI on outcomes begins early and persists throughout the posttransplant period. Donor obesity status is an independent risk factor for inferior long-term renal allograft outcome despite adjusting for donor and recipient size mismatch and other donor, recipient, and transplant factors.

The human microbiome plays an important role in our health and identifying factors associated with microbiome composition provides insights into inherent disease mechanisms. By amplifying and sequencing the marker genes in high‐throughput sequencing, with highly similar sequences binned together, we obtain operational taxonomic units (OTUs) profiles for each subject. Due to the high‐dimensionality and nonnormality features of the OTUs, the measure of diversity is introduced as a summarization at the microbial community level, including the distance‐based beta‐diversity between individuals. Analyses of such between‐subject attributes are not amenable to the predominant within‐subject‐based statistical paradigm, such as t‐tests and linear regression. In this paper, we propose a new approach to model beta‐diversity as a response within a regression setting by utilizing the functional response models (FRMs), a class of semiparametric models for between‐ as well as within‐subject attributes. The new approach not only addresses limitations of current methods for beta‐diversity with cross‐sectional data, but also provides a premise for extending the approach to longitudinal and other clustered data in the future. The proposed approach is illustrated with both real and simulated data.