PRPF6 promotes androgen receptor/androgen receptor-variant 7 actions in castration-resistant prostate cancer cells

Androgen receptor (AR) and its variants play vital roles in development and progression of prostate cancer. To clarify the mechanisms involved in the enhancement of their actions would be crucial for understanding the process in prostate cancer and castration-resistant prostate cancer transformation. Here, we provided the evidence to show that pre-mRNA processing factor 6 (PRPF6) acts as a key regulator for action of both AR full length (AR-FL) and AR variant 7 (AR-V7), thereby participating in the enhancement of AR-FL and AR-V7-induced transactivation in prostate cancer. In addition, PRPF6 is recruited to cis-regulatory elements in AR target genes and associates with JMJD1A to enhance AR-induced transactivation. PRPF6 also promotes expression of AR-FL and AR-V7. Moreover, PRPF6 depletion reduces tumor growth in prostate cancer-derived cell lines and results in significant suppression of xenograft tumors even under castration condition in mouse model. Furthermore, PRPF6 is obviously highly expressed in human prostate cancer samples. Collectively, our results suggest PRPF6 is involved in enhancement of oncogenic AR signaling, which support a previously unknown role of PRPF6 during progression of prostate cancer and castration-resistant prostate cancers.     

Modeling vegetation greenness and its climate sensitivity with deep‐learning technology

Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep‐learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature.     

The efficacy of DNA barcoding in the classification,genetic differentiation,and biodiversity assessment of benthic macroinvertebrates

Macroinvertebrates have been recognized as key ecological indicators of aquatic environment and are the most commonly used approaches for water quality assessment. However, species identification of macroinvertebrates (especially of aquatic insects) proves to be very difficult due to the lack of taxonomic expertise in some regions and can become time‐consuming. In this study, we evaluated the feasibility of DNA barcoding for the classification of benthic macroinvertebrates and investigated the genetic differentiation in seven orders (Insecta: Ephemeroptera, Plecoptera, Trichoptera, Diptera, Hemiptera, Coleoptera, and Odonata) from four large transboundary rivers of northwest China and further explored its potential application to biodiversity assessment. A total of 1,144 COI sequences, belonging to 176 species, 112 genera, and 53 families were obtained and analyzed. The barcoding gap analysis showed that COI gene fragment yielded significant intra‐ and interspecific divergences and obvious barcoding gaps. NJ phylogenetic trees showed that all species group into monophyletic species clusters whether from the same population or not, except two species (Polypedilum. laetum and Polypedilum. bullum). The distance‐based (ABGD) and tree‐based (PTP and MPTP) methods were utilized for grouping specimens into Operational Taxonomic Units (OTUs) and delimiting species. The ABGD, PTP, and MPTP analysis were divided into 177 (p = .0599), 197, and 195 OTUs, respectively. The BIN analysis generated 186 different BINs. Overall, our study showed that DNA barcoding offers an effective framework for macroinvertebrate species identification and sheds new light on the biodiversity assessment of local macroinvertebrates. Also, the construction of DNA barcode reference library of benthic macroinvertebrates in Eurasian transboundary rivers provides a solid backup for bioassessment studies of freshwater habitats using modern high‐throughput technologies in the near future.     

IL-15Rα deficiency leads to mitochondrial and myofiber differences in fast mouse muscles

The purpose of this study was to determine mitochondrial changes in fast muscles from interleukin-15 receptor alpha knockout (IL-15RαKO) mice. We tested the hypothesis that fast muscles from IL-15RαKO mice would have a greater mitochondrial density and altered internal structure compared to muscles from control mice. In fast muscles from IL-15RαKO mice, mitochondrial density was 48% greater with a corresponding increase in mitochondrial DNA content. Although there were no differences in the relative size of isolated mitochondria, internal complexity was lower in mitochondria from IL-15RαKO mice. These data support an increase in mitochondrial biogenesis and provide direct evidence for a greater density and altered internal structure of mitochondria in EDL muscles deficient in IL-15Rα.     

Hopeahainol A attenuates memory deficits by targeting β‐amyloid in APP/PS1 transgenic mice

Increasing evidence demonstrates that amyloid beta (Aβ) elicits mitochondrial dysfunction and oxidative stress, which contributes to the pathogenesis of Alzheimer's disease (AD). Identification of the molecules targeting Aβ is thus of particular significance in the treatment of AD. Hopeahainol A (HopA), a polyphenol with a novel skeleton obtained from Hopea hainanensis, is potentially acetylcholinesterase‐inhibitory and anti‐oxidative in H₂O₂‐treated PC12 cells. In this study, we reported that HopA might bind to Aβ_1–42 directly and inhibit the Aβ_1–42 aggregation using a combination of molecular dynamics simulation, binding assay, transmission electron microscopic analysis and staining technique. We also demonstrated that HopA decreased the interaction between Aβ_1–42 and Aβ‐binding alcohol dehydrogenase, which in turn reduced mitochondrial dysfunction and oxidative stress in vivo and in vitro. In addition, HopA was able to rescue the long‐term potentiation induction by protecting synaptic function and attenuate memory deficits in APP/PS1 mice. Our data suggest that HopA might be a promising drug for therapeutic intervention in AD.