In this study, we show that HuR destabilizes p16 INK4 mRNA. Although the knockdown of HuR or AUF1 increased p16 expression, concomitant AUF1 and HuR knockdown had a much weaker effect. The knockdown of Ago2, a component of the RNA-induced silencing complex (RISC), stabilized p16 mRNA. The knockdown of HuR diminished the association of the p16 3 untranslated region (3UTR) with AUF1 and vice versa. While the knockdown of HuR or AUF1 reduced the association of Ago2 with the p16 3UTR, Ago2 knockdown had no influence on HuR or AUF1 binding to the p16 3UTR. The use of EGFP-p16 chimeric reporter transcripts revealed that p16 mRNA decay depended on a stem-loop structure present in the p16 3UTR, as HuR and AUF1 destabilized EGFP-derived chimeric transcripts bearing wild-type sequences but not transcripts with mutations in the stem-loop structure. In senescent and HuR-silenced IDH4 human diploid fibroblasts, the EGFPp16 3UTR transcript was more stable. Our results suggest that HuR destabilizes p16 mRNA by recruiting the RISC, an effect that depends on the secondary structure of the p16 3UTR and requires AUF1 as a cofactor.

Understanding metabolism is indispensable to unraveling the mechanistic basis of many physiological and pathological processes. However, in situ metabolic imaging tools are still lacking. Herein we introduce a framework for mid-infrared (MIR) metabolic imaging by coupling the emerging high-information-throughput MIR microscopy with specifically designed IR-active vibrational probes. Three categories of small vibrational tags including azide bond, 13 C-edited carbonyl bond and deuterium-labeled probes are presented to interrogate various metabolic activities in cells, small organisms and mice. Two MIR imaging platforms are implemented including broadband Fourier transform infrared (FTIR) microscopy and discrete frequency infrared (DFIR) microscopy with a newly incorporated spectral region (2000–2300 cm −1 ). Our technique is uniquely suited for metabolic imaging with high-information-throughput. In particular, we performed single-cell metabolic profiling including heterogeneity characterization, and large-area metabolic imaging at tissue/organ level with rich spectral information.

Elevated levels of RNA binding protein HuR were found in various human cancers. However, the mechanisms underlying HuR over-expression in cancers have not been fully elucidated. Here, we show that miR-16 acts as a novel post-transcriptional regulator for HuR. Knockdown of miR-16 increased HuR protein levels in MDA-MB-231 cells, while over-expression of pre-miR16 reduced HuR expression. Neither knockdown nor over-expression of miR-16 could alter the mRNA levels of HuR. Instead, knockdown of miR-16 increased the level of de novo synthesized HuR protein. Importantly, mechanistic studies showed that miR-16 associated with the 3'UTR of HuR, and knockdown of miR-16 markedly increased the luciferase activity of a HuR 3'UTR-containing reporter. We further demonstrate that the level of miR-16 was inversely correlated with HuR protein level in human breast carcinoma. Together, our results suggest an important role of miR-16 in regulating HuR translation and link this regulatory pathway to human breast cancer.

The RNA-binding protein, HuR, associates with the HuR mRNA, but the consequences of this interaction are unknown. Here, we use human diploid fibroblasts (HDFs) and cervical carcinoma cells to study this regulatory paradigm. Ectopic overexpression of HuR potently enhanced the translation and cytoplasmic levels of endogenous HuR, but did not affect HuR mRNA levels. Inhibition of CRM1 function by Lemptomycin B or by knockdown of CRM1 greatly diminished the cytoplasmic levels of endogenous HuR mRNA and hence blocked the induction of endogenous HuR by exogenous HuR. Further studies showed that HuR interacted with the 3′-untranslated region (UTR) of HuR and that overexpression of HuR increased the cytoplasmic levels of a chimeric luciferase-HuR 3′-UTR reporter transcript, as well as luciferase activity; conversely, HuR knockdown reduced both parameters. Moreover, the loss of HuR in senescent, late-passage HDFs was accompanied by a reduced cytoplasmic presence of endogenous HuR mRNA, ectopic Luc-HuR-3′UTR reporter transcript, and luciferase activity relative to what was observed in young, early-passage cells. Our results reveal a positive feedback mechanism for the regulation of HuR, which may play an important role in the regulation of HuR during replicative senescence.

We present a machine learning approach that uses data from smartphones and fitness trackers of 138 college students to identify students that experienced depressive symptoms at the end of the semester and students whose depressive symptoms worsened over the semester. Our novel approach is a feature extraction technique that allows us to select meaningful features indicative of depressive symptoms from longitudinal data. It allows us to detect the presence of post-semester depressive symptoms with an accuracy of 85.7% and change in symptom severity with an accuracy of 85.4%. It also predicts these outcomes with an accuracy of >80%, 11–15 weeks before the end of the semester, allowing ample time for pre-emptive interventions. Our work has significant implications for the detection of health outcomes using longitudinal behavioral data and limited ground truth. By detecting change and predicting symptoms several weeks before their onset, our work also has implications for preventing depression.

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BackgroundHuman papillomaviruses (HPVs) are strongly associated with the development of cervical carcinoma, and the distribution of HPV genotypes varies regionally.MethodsTo investigate the distribution characteristics of different genotypes of HPV infection in women in Wuhan, China, a total of 13 775 patients were enrolled over 2 years.ResultsOf these, 2436 patients were infected with HPVs, and the total infection rate was 17.68%. The infection rate of high‐risk HPV (HR‐HPV) was significantly higher (13.96%) than that of single low‐risk HPV (LR‐HPV; 3.72%). Among the HR‐HPV infections, the most common genotype was HPV 52 with an infection rate of 4.23%, followed by HPVs 16, 58, 39, and 51. The most common LR‐HPV genotype was HPV 81, followed by HPVs 6, 11, and 44. Patients under the age of 25 years were found to have the highest HPV infection rate (P < .05). After the age range of 51‐55 years, a downward trend in total HPVs and HR‐HPVs was observed. The HPV infection rate for a single genotype was higher than that for multiple HPVs (P < .01), and the detection rates in summer and winter were significantly higher than those in spring and autumn.ConclusionsThe results demonstrate that the distribution characteristics of various HPV genotype infections are associated with region and age and may be related to season. These data could be the basis for further epidemiological analysis into the control and prevention of HPV infection in this region.